Soybean - Molecular Aspects of Breeding 978-953-307-240-1

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SOYBEAN MOLECULAR ASPECTS OF BREEDING Edited by Aleksandra Sudarić

Soybean - Molecular Aspects of Breeding Edited by Aleksandra Sudarić

Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2011 InTech All chapters are Open Access articles distributed under the Creative Commons Non Commercial Share Alike Attribution 3.0 license, which permits to copy, distribute, transmit, and adapt the work in any medium, so long as the original work is properly cited. After this work has been published by InTech, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book. Publishing Process Manager Katarina Lovrecic Technical Editor Teodora Smiljanic Cover Designer Martina Sirotic Image Copyright George Burba, 2010. Used under license from Shutterstock.com First published March, 2011 Printed in India A free online edition of this book is available at www.intechopen.com Additional hard copies can be obtained from [email protected]

Soybean - Molecular Aspects of Breeding, Edited by Aleksandra Sudarić p. cm. ISBN 978-953-307-240-1

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Contents Preface Part 1

IX

Molecular Biology and Biotechnology 1

Chapter 1

Protein Expression Systems: Why Soybean Seeds? 3 Kenneth Bost and Kenneth Piller

Chapter 2

Optimizing Recombinant Protein Expression in Soybean 19 Laura C. Hudson, Kenneth L. Bost and Kenneth J. Piller

Chapter 3

Virus-Induced Gene Silencing of Endogenous Genes and Promotion of Flowering in Soybean by Apple latent spherical virus-Based Vectors 43 Noriko Yamagishi and Nobuyuki Yoshikawa

Chapter 4

Genetic Improvement: Molecular-Based Strategies Aleksandra Sudarić, Marija Vratarić, Snežana Mladenović Drinić, and Zvonimir Zdunić

Chapter 5

Integration of Major QTLs of Important Agronomic Traits in Soybean Guohua Hu, Qingshan Chen, Chunyan Liu, Hongwei Jiang, Jialin Wang and Zhaoming Qi

57

81

Chapter 6

A Versatile Soybean Recombinant Inbred Line Population Segregating for Low Linolenic Acid and Lipoxygenase Nulls - Molecular Characterization and Utility for Soymilk and Bioproduct Production 119 Yarmilla Reinprecht, Shun-Yan Luk-Labey and K. Peter Pauls

Chapter 7

Breeding for Promiscuous Soybeans at IITA Hailu Tefera

Chapter 8

Characterization of Soybean-Nodulating Rhizobial Communities and Diversity 163 Yuichi Saeki

147

VI

Contents

Part 2 Chapter 9

Breeding for Abiotic Stress

185

Proteomics Approach for Identifying Abiotic Stress Responsive Proteins in Soybean 187 Mohammad-Zaman Nouri, Mahmoud Toorchi and Setsuko Komatsu

Chapter 10

Molecular Responses to Osmotic Stresses in Soybean Tsui-Hung Phang, Man-Wah Li, Chun-Chiu Cheng, Fuk-Ling Wong, Ching Chan and Hon-Ming Lam

Chapter 11

Genotypic Influence on the Absorption, Use and Toxicity of Manganese by Soybean 241 Andre Rodrigues dos Reis and Jose Lavres Junior

Part 3

Breeding for Biotic Stress

215

259

Chapter 12

Resistance to Pythium Seedling Disease in Soybean Rupe, J.C., Rothrock, C.S., Bates, G., Rosso, M. L., Avanzato, M. V. and Chen, P.

Chapter 13

Phomopsis Seed Decay of Soybean Shuxian Li

Chapter 14

Soybean Rust: Five Years of Research 293 Arianne Tremblay

Chapter 15

Detection, Understanding and Controlof Soybean Mosaic Virus 335 Xiaoyan Cui, Xin Chen and Aiming Wang

Chapter 16

Evolution of Soybean Aphid Biotypes: Understanding and Managing Virulence to Host-Plant Resistance 355 Andrew P. Michel, Omprakash Mittapalli and M. A. Rouf Mian

Chapter 17

Evaluation and Utilization of Soybean Germplasm for Resistance to Cyst Nematode in China 373 Ying-Hui Li, Xiao-Tian Qi, Ruzhen Chang and Li-Juan Qiu

Chapter 18

Cell-Specific Studies of Soybean Resistance to Its Major Pathogen, the Soybean Cyst Nematode as Revealed by Laser Capture Microdissection, Gene Pathway Analyses and Functional Studies 397 Vincent P. Klink, Prachi D. Matsye and Gary W. Lawrence

Chapter 19

Genetically Modified Soybean for Insect-Pests and Disease Control 429 Maria Fatima Grossi-de-Sa, Patrícia B. Pelegrini and Rodrigo R. Fragoso

261

277

Contents

Part 4

Recent Technology 453

Chapter 20

Spectral Characteristics of Soybean during the Vegetative Cycle Using Landsat 5/TM Images in The Western Paraná, Brazil 455 Erivelto Mercante, Rubens A. C. Lamparelli, Miguel A. Uribe-Opazo and Jansle Viera Rocha

Chapter 21

Bio-Based Nanocomposites Composed of Photo-Cured Soybean-Based Resins and Supramolecular Hydroxystearic Acid Nanofibers Mitsuhiro Shibata

Chapter 22

Transgenic Residues in Soybean-based Foods 495 Mónica L. Chávez-González, Carolina Flores-Gallegos, Víctor M. García-Lazalde, Cristóbal Noé Aguilar and Raúl Rodríguez-Herrera

473

VII

Preface Soybean (Glycine max (L.) Merr.) is the leading oil and protein crop of the world, which is used as a source of high quality edible oil, protein and livestock feed. Various functional components derived from secondary metabolite have also received significant atention in terms of human health. Over the past three decades, the scientific and technological developments in most regions have increased soybean production on the global level. Nevertheless, soybean breeding has undoubtedly played a key role in production increases. Conventional breeding strategies have been very successful in improving soybean productivity and quality. In practice today, the field of soybean breeding is in transition and changing rapidly. The incorporation of molecular aspects of genetic analysis and molecular marker-assisted selection is critical to understanding soybean breeding strategies and practices. Scientific discoveries in the area of structural and functional plant genomics lead to development of new soybean varieties with advanced nutritive properties and yield enhancement through greater resistance to various abiotic and biotic factors, beter adapted to new market, production and environment demands. Based on the availability and combination of conventional and molecular technologies, a substantial increase in the rate of genetic gain for economically important soybean traits can be predicted in the next decade. The book Soybean - Molecular Aspects of Breeding focuses on recent progress in our understanding of the genetics and molecular biology of soybean and provides a broad review of the subject, from genome diversity to transformation and integration of desired genes using current technologies. This book is divided into four parts (Molecular Biology and Biotechnology, Breeding for Abiotic Stress, Breeding for Biotic Stress, Recent Technology) and contains 22 chapters. Part I, “Molecular Biology and Biotechnology”, (Chapters 1 to 8) focuses on advances in molecular biology and laboratory procedures that have been developed recently to manipulate DNA and provide new genes of interest to soybean breeder. Chapter 1 considers the transgenic soybean seed as a unique platform for the expression and accumulation of desired proteins. Chapter 2 focuses on incorporating current knowledge for optimizing recombinant protein expression in soybeans. In Chapter 3, the authors describe the use of Apple Latent Spherical Virus (ALSV) vector for Virus-Induced Gene Silencing (VIGS) of endogenous genes at all growth stages of soybean plants and seeds. Chapter 4 reviews the technologies for molecular marker analysis and achievements in the area of genetic transformation (genetic modification) in soybean. Chapter 5 points out on the QTL metaanalysis for major agronomic traits in soybean (oil content, protein content, faty acid, amino acid content, isoflavone content, fungal diseases resistance, insect resistance, cyst

X

Preface

nematode resistance, 100-seed weight, lodging, plant height, growth stages). Chapter 6 describes development of a versatile soybean recombinant inbred line population segregating for low linolenic acid and lypoxygenase nulls, its molecular characterization and utility for soymilk and composite material production. Chapter 7 introduces the achievements of soybean breeding work at the International Institute of Tropical Agriculture (Malawi) with emphasis on enhancement biological nitrogen fixation capacity of new breeding lines through the promiscuity approach as well as matching genotypes with effective inoculants strains. Characterization of soybean-nodulating rhizobial communities and its diversity is the subject of Chapter 8. Part II, “Breeding for abiotic stress” (Chapters 9 to 11) covers proteomics approaches form as a powerful tool for investigating the molecular mechanisms of the plant responses to various types of abiotic stresses. It provides a path toward increasing the efficiency of indirect selection for inherited traits. Chapter 9 describes recent methodologies for the extraction of proteins from soybean and then protein identification techniques related to the abiotic stresses. Chapter 10 is centered on the common and specific components of various types of osmotic stresses, tolerant germplasm-specific components, the current obstacles in this research area and the forward looking research strategies to tackle these problems. Studying anatomical and ultrastructural changes in response to manganese (Mn) nutritional disorders and deleterious effects of Mn stress on soybean is the subject of Chapter 11. Part III, “Breeding for biotic stress” (Chapters 12 to 19) addresses issues related to application of molecular based strategies in order to increase soybean resistance to various biotic factors (pathogens, insects, nematode). Chapter 12 reports the resistance to a number of Pythium spp. that should be useful in reducing the risk of stand loss due to this group of pathogens. Chapter 13 introduces Phomopsis Seed Decay (PSD) with emphasis on application of SSR marker in identification resistant germplasm and using of the resistant cultivars as the most effective method for controlling PSD. Chapter 14 focuses on identification approaches to broaden the resistance to soybean rust caused by pathogen Phakospora pachyrhizi. In Chapter 15, authors describe Soybean Mosaic Virus (SMV), interaction between SMV and plant, as well as its current and future control strategies. Chapter 16 reviews the current status of soybean aphid biotypes and strategies for understanding and managing virulence to host-plant resistance. Chapter 17 considers the advances of identification for resistance to cyst nematode, discovery novel gene from the resistant accession and resistant cultivar development. Cell-specific studies of soybean resistance to the cyst nematode as revealed by laser capture microdissection, gene pathway analyses and functional studies are the subject of Chapter 18. In Chapter 19, authors describe biotechnological insights using different molecules in order to decrease biotic stresses in soybean field. Part IV, “Recent Technology” (Chapters 20 to 22) reviews newer technologies into the realm of soybean monitoring, processing and product use. Studying the changes in the spectral behavior of the soybean crop, during the vegetative cycle, by spectral-temporal profiles of the mapped crop areas, using two vegetation indexes (Normalized Difference Vegetation Index-NDVI; Greenness Vegetation Index-GVI) of multispectral images from the satellite Landsat 5/TM is the subject of Chapter 20. Chapter 21 describes the preparation and properties of the bio-nanocomposites composed of the ESO (epoxidized soybean oil) and AESO (acrylated epoxidized soybean oil) crosslinked by the photo-

Preface

polymerization and self-assembled HAS (hydroxystearic acid) molecules. Chapter 22 comments on a number of procedures for detection of transgenic residues in soybeanbased foods. It becomes a very actual theme because the increase of products derived from genetically modified organisms in the market shelves and the consumer demands for more strict regulations about labeling of this kind of products. Each chapter of this book presents an excellent overview of a broad range of topics. The references at the end of each chapter provide a starting point to acquire a deeper knowledge on the state-of-the-art. While the information accumulated in this book is of primary interest to plant breeders, valuable insights are also offered to agronomists, molecular biologists, physiologists, plant pathologists, food scientists and students with an interest in plant breeding. The book is a result of efforts by many experts from different countries. I would like to acknowledge each of the authors who devoted much time and effort in delivering their chapter to this volume. We hope that this book will contribute to bringing about more informed modern techniques used in soybean breeding.

Aleksandra Sudarić Agricultural Institute Osijek Osijek, Croatia

XI

Part 1 Molecular Biology and Biotechnology

1 Protein Expression Systems: Why Soybean Seeds? Kenneth Bost and Kenneth Piller University of North Carolina at Charlotte and SoyMeds, Inc. United States of America 1. Introduction The global protein therapeutics market is approaching $100 billion in annual sales. Furthermore, the in vitro diagnostic market, which relies heavily on the use of recombinant proteins as analyte specific reagents, is approaching $50 billion in annual sales. Increases in each market sector are estimated to be 8% to 20% per year for the next decade. Therefore, the costs of protein-based therapeutics and diagnostics will continue to be a significant percentage of health care expenses for patients and for agricultural animals and pets. A platform technology, which produces recombinant proteins at greatly reduced costs, provides inherent advantages, and allows low-tech sustainability of product lines, represents a competitive innovation which could create significant wealth in this industry. The use of soybean-derived proteins has the potential to provide such advantages. Since the use of recombinant proteins is widespread in human and animal therapeutics and diagnostics, there are thousands of potential applications for such a platform technology. While no one technology is optimal for the expression of every transgenic protein (Brondyk, 2009), the research that we have performed to date demonstrates the utility and feasibility of commercial applications for proteins made in transgenic soybean seeds. Soybean seeds expressing transgenic proteins represent a novel, sustainable platform technology which overcomes some of the current limitations for producing recombinant proteins for diagnostics, therapeutics, and industrial applications. Advantages include low cost of glycoslyated protein production, greenhouse containment, highest protein/biomass ratio, marketable formulations which require no purification from soy, safety, accurate dosing, low cost of protein purification, low-tech sustainability of product lines, reduced risk of contamination, ease of scalability, minimal waste produced, as well as being a green technology. To our knowledge, no other protein expression technology compares. Despite the fact that the present protein therapeutic and diagnostic markets are growing rapidly, some applications continue to be limited by the current technologies employed (Brondyk, 2009). For example the cost of production and purification of certain recombinant proteins makes their use in particular applications non-profitable. Some proteins cannot be expressed by any current technology and require expensive purification from human or animal tissues. A platform technology which could alleviate these concerns would create significant opportunities for novel product development, or expanded applications for existing products. In short, there is a need for alternative technologies for expressing and purifying recombinant proteins which can advance their applications. We propose that

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Soybean - Molecular Aspects of Breeding

soybean seeds expressing transgenic proteins represent a platform technology that can be a solution for many of these problems. Recent research provides a strong basis for this supposition. These results support the utility of this technology, have demonstrated its advantages, and suggest additional benefits that are currently being explored.

2. Utility of glycosylated proteins produced in transgenic soybean seeds Roundup Ready soybeans were one of the first examples of a commercially viable transgenic plant (Padgette et al., 1995). These transgenic soybeans express a functional enzyme, 5-enolpyruvylshikimate-3-phosphate synthase, making the plants tolerant to the herbicide, RoundupTM. Presently, approximately 90% of the soybeans farmed in the United States are Roundup Ready. In addition to this value-added trait, there have been several recent successes in modifying soy crop lines aimed at imparting some commercial advantage. Transgenic soybean lines expressing the cry1A gene provide protection against Lepidopteran species (McPherson & MacRae, 2009). Transgenic soybean lines expressing an active APase, demonstrated increased phosphorus content when grown in soils with limited phosphate content (Wang et al., 2009). Alterations in seed oil content have been achieved using transgenic soybean plants expressing genes having lysophosphatidic acid acyltransferase activity (Rao & Hildebrand, 2009). Over-expression of an aspartate kinase in transgenic seeds allowed increased threonine levels to occur (Qi et al., 2010). While these examples are not a comprehensive listing of value-added traits that require the expression of a function enzymatic protein, they serve to demonstrate the amenability of soybean seeds to such transformations. While value-added traits are being exploited, transgenic soybean seeds have also been used as bioreactors to express a variety of foreign proteins. For example, Zeitlin, et al. (Zeitlin et al., 1998) successfully expressed functional antibodies against herpes simplex virus-2 glycoprotein B in transgenic soybeans. More recently, proinsulin has been expressed and the storage vacuoles can accumulate mature polypeptide in these seed lines (Cunha et al., 2010). In our laboratories, we have successfully expressed the subunit protein antigens, E. coli, FanC (Garg et al., 2007; Oakes, Bost, & Piller, 2009; Piller et al., 2005), non-toxic, mutant forms of several bacterial toxins, and potential immunomodulatory proteins in transgenic soybean seeds, and are evaluating their usefulness as therapeutics. We have also successfully expressed particular full-length human proteins, and ongoing studies are aimed at demonstrating their substantial equivalence for use as analyte specific reagents in diagnostic assays. While this is not an exhaustive listing of the plant and foreign proteins which have been expressed in transgenic soybean seeds, these examples demonstrate the utility of this platform technology. There are several reasons for the success of such endeavors, but perhaps the most important lies in the biology of the soybean seed itself. One of the most important functions of the seed is to express and package proteins. This entails posttranslational modifications, including glycosylation, and packaging which not only allows proper folding, but also provides an environment for stable, long-term protein storage. This conclusion is supported by the fact that many of the transgenic proteins expressed have enzymatic activity, ability to bind antigen, or the ability to be recognized by monoclonal antibodies specific for their native counterparts.

Protein Expression Systems: Why Soybean Seeds?

5

3. Soybean seeds represent the highest protein to biomass ratio Soybean seeds, by weight, are 40% protein with approximately 20% oil, 35% carbohydrates, and 5% ash (Liu, 1999). Most of the normal soy seed proteins are heat-stable and desiccantresistant, in keeping with the ability of soybeans to remain germinate-capable following years of storage in ambient conditions. Soybean plants can produce as much as twice the protein per acre of any other major crop (see: http://www.soyatech.com/soy_health.htm). Protein production by soy is also more efficient than animal-derived protein when factoring the acreage required for grazing or feeding. As we will discuss below, this fundamental characteristic of soybean seeds to produce and store large amounts of protein may be exploited as a platform technology for expression.

4. High yield translates into a potential for low cost protein production Present and future use of recombinant proteins will be limited in large part by their cost of production. Presently, the expense of expressing and purifying some recombinant proteins prohibits or limits their practical or realistic use. This is true for some therapeutic, as well as diagnostic, applications in westernized societies, and such barriers are even greater for developing countries. Unless this economic burden can be overcome, barriers for product development will remain. For some applications, too much recombinant protein is needed such that the cost is prohibitive. For some applications, elaborate purification schemes make particular proteins unaffordable. For some applications, there is no source of some recombinant proteins, requiring isolation from human or animal tissues. A platform technology which could alleviate these concerns would create significant opportunities for novel product development, or expanded applications for existing products, and therefore, create significant wealth. Decisions to develop commercial products which include such proteins will depend largely on the practicality of having a cost-sustainable platform technology. Theoretically, expression of transgenic proteins in soybean seeds represents one of the most cost-efficient platforms, and recent work has demonstrated potential economic advantages. Presently we (Garg, et al., 2007; Oakes, Bost, et al., 2009; Piller, et al., 2005), and others (Cunha, et al., 2010; Ding, Huang, Wang, Sun, & Xiang, 2006; Moravec, Schmidt, Herman, & Woodford-Thomas, 2007; Qi, et al., 2010; Rao & Hildebrand, 2009; Wang, et al., 2009; Zeitlin, et al., 1998), have developed stable soybean lines that express 1% to 4% of their total soluble protein as the transgenic protein. Since soybean seeds are 40% protein by weight, an average sized seed weighing approximately 150 milligrams represents approximately 2.4 milligrams of transgenic protein per seed at 4% expression (Table 1). As will be discussed below, soybeans can easily be converted into soy powder, and, one liter of this powder totals approximately 800 grams of seed material. At 4% expression, this one liter of soy powder contains approximately 12.8 grams of the unpurified, transgenic protein (Table 1). It is useful to compare this production with that for a liter of broth from bacterial (Zerbs, Frank, & Collart, 2009), yeast (Cregg et al., 2009), insect (Jarvis, 2009), mammalian (Geisse & Fux, 2009), or plant (Hellwig, Drossard, Twyman, & Fischer, 2004; Lienard, Sourrouille, Gomord, & Faye, 2007) cell culture. Since retail costs of commercially available recombinant proteins can easily be hundreds to thousands of dollars per milligram, extrapolations presented in Table 2 are enlightening. One liter, or 800 grams, of soy powder could represent as much as 12.8 grams of transgenic protein

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Soybean - Molecular Aspects of Breeding

at an expression level of 4%. As shown in Table 2, potentially, as little as one liter of total soy protein could contain the equivalent of millions of dollars of unpurified transgenic protein. Percent expression of the transgenic protein 1% 2% 4% 8%

Milligrams of transgenic protein per seed (150 milligrams) 0.6 milligrams 1.2 milligrams 2.4 milligrams 4.8 milligrams

Grams of transgenic protein per liter (800 grams of soy powder) 3.2 grams 6.4 grams 12.8 grams 25.6 grams

Table 1. Estimated amounts of transgenic protein per seed or per one liter of soy powder Retail commercial cost of a theoretical recombinant protein per milligram $10 $100 $1000 $10,000

Linear extrapolation of commercial value for 12.8 grams of the theoretical recombinant protein $128,000 $1,280,000 $12,800,000 $128,000,000

Table 2. Extrapolation of the potential value for one liter of soy protein powder expressing a particular transgenic protein at a level of 4% Further extrapolations can be made for bulk production of transgenic soybeans in secure greenhouses. Such greenhouses provide containment and controlled conditions which allow optimal growth for maximal yields. Using such conditions, it is not difficult to obtain 60 bushels of soybeans per greenhouse acre (see http://www.soystats.com). The industry standard for an average weight of a bushel of soybeans is 60 pounds, with an average quantity of 2500 seeds per pound. This computes to approximately 9 million transgenic soybean seeds per acre. At an average weight of 150 milligrams per seed, this represents approximately 1,350 kilograms of seeds or 540 kilograms of total soy protein. As shown in Table 3, at already achieved 4% expression levels, this calculates to 21.6 kilograms of transgenic protein per greenhouse acre. Percentage expression of the transgenic protein

Average estimated quantity of soybean protein per greenhouse acre

1%

540 kilograms total soy protein

2%

540 kilograms total soy protein

4%

540 kilograms total soy protein

8%

540 kilograms total soy protein

Calculated quantity of transgenic protein per greenhouse acre 5.4 kilograms of transgenic protein 10.8 kilograms of transgenic protein 21.6 kilograms of transgenic protein 43.2 kilograms of transgenic protein

Table 3. Estimated quantities of transgenic protein per greenhouse acre based on percent expression

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Protein Expression Systems: Why Soybean Seeds?

It should be noted that we have included 8% expression levels of the transgenic protein in Tables 1 and 3. While we have yet to achieve such levels in our laboratory, the use of transgenic soybeans to express foreign proteins is evolving [e.g. (Schmidt & Herman, 2008)]. Recent DNA sequencing of the soybean genome (Hyten et al., 2010; Schmutz et al., 2010), the development of better promoters, engineering of high protein-expressing seeds, and other coming advances, promise to further increase the efficiency of expression of transgenic proteins using this platform technology. Therefore it seems reasonable to conclude that future advances will only facilitate our ability to increase the level of protein expression. While future advances promise even higher percentages of transgenic protein expression, current levels will permit contained greenhouse to produce bulk quantities of particular proteins. Using theoretical costs for a recombinant protein as before, Table 4 extrapolates the potential value for an acre of greenhouse grown soybeans at an expression level of 4%. Again, these numbers serve to underscore the potential for high capacity of transgenic protein production using a confined growth space. Retail commercial cost of a theoretical recombinant protein per milligram $10 $100 $1,000 $10,000

Calculated quantity of transgenic protein per greenhouse acre at 4% expression 21.6 kilograms of transgenic protein 21.6 kilograms of transgenic protein 21.6 kilograms of transgenic protein 21.6 kilograms of transgenic protein

Linear extrapolation for the potential value of 21.6 kilograms of the theoretical recombinant protein $216,000,000 $2,160,000,000 $21,600,000,000 $216,000,000,000

Table 4. Extrapolation of the potential value for an acre of greenhouse soybeans expressing a particular transgenic protein at a level of 4%

5. Greenhouse containment for growing transgenic soybeans As shown in Tables 1, 2, 3, and 4, it is clear that at expression levels already obtained (e.g. 1% - 4%) there will be little reason to grow such transgenic plants in open fields. At production levels of 3-10 kilograms per acre, and with the potential for 3 separate growing seasons per year, propagation in contained greenhouses would impart few limitations, even for bulk production. Despite the fact the soybean plant is self pollinating, secure greenhouse growth would provide additional containment by eliminating transgenic seed escape (Traynor, 2001). As will be discussed below, processing of soybean seeds to soy powder can easily be accomplished in the greenhouse prior to removal of this non-germinating material for formulation and/or protein purification. Such containment procedures, therefore, provide management of these genetically modified crops from release into the environment. Propagation of transgenic soybean plants in secure greenhouses also provides advantages in addition to containment. Therapeutic and diagnostic proteins must be produced with good manufacturing practices (GMP) as dictated by approval agencies. Greenhouse growth

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Soybean - Molecular Aspects of Breeding

allows for ease of standard operating procedures to be implemented with respect to growth conditions, disease and pathogen monitoring, harvesting, and quality control. Stated simply, there are numerous advantages for greenhouse growth, and very few reasons for proposing open field propagation of transgenic soybean plants, that are destined to produce proteins for therapeutic and diagnostics purposes.

6. Costs to grow an acre of transgenic soybeans in contained greenhouses The efficiency with which soybean plants can be grown in the field or in greenhouses is well documented (Traynor, 2001). This understanding of maximizing crop yields serves to reduce the cost of producing seeds from transgenic plants. Current costs per acre to plant and harvest soybeans from open fields range from $300 to $600 per acre depending upon planting conditions, treatments, geographic area, etc. It has been estimated that greenhouse containment for soybean growth at Biosafety Level 2 (BSL-2) would increase this cost approximately 20 fold (Traynor, 2001). Despite this increased cost and the benefits that go with greenhouse production, the total expense for planting and harvesting remains a reasonable $6,000 to $12,000 per acre. Based on these estimates, it is possible to project a cost per milligram for production and harvest of soybeans expressing a transgenic protein using BSL-2 conditions (Table 5). Percentage expression of the transgenic protein 1% 2% 4% 8%

Calculated quantity of transgenic protein per greenhouse acre 5.4 kilograms of transgenic protein 10.8 kilograms of transgenic protein 21.6 kilograms of transgenic protein 43.2 kilograms of transgenic protein

Costs per milligram of protein assuming $12,000 production costs $ 0.002 per milligram $ 0.001 per milligram $ 0.0005 per milligram $ 0.00025 per milligram

Table 5. Cost production projections per milligram of transgenic protein using BSL-2 greenhouse conditions and assuming $12,000 per acre total production costs It is clear to see the potential for large scale production of transgenic proteins at a very low cost of production when one considers such calculations.

7. Formulations which require no purification of the transgenic protein from soy The cost projections in Table 5 pertain only to the growth and harvesting of soybean seeds expressing the transgenic protein. As with all protein expression systems, additional costs are required for purification of the protein of interest. However, before we discuss purification costs and the advantages of soybean-derived proteins, an intriguing possibility is the use of formulations made from transgenic soybean seeds which would require no purification of the protein prior to its use.

Protein Expression Systems: Why Soybean Seeds?

9

Soybeans can be formulated into a variety of consumables. Simply grinding soybeans into a powder (e.g. soy powder) is sufficient for some agricultural purposes, including the addition to feedstocks. However soy powder is routinely solvent extracted to remove oils, followed by moist heat and drying to produce soybean meal, which can also be consumed. Other formulations include soy flour which is made from finely ground, defatted beans, followed by removal of solvents and drying. Perhaps the most recognizable formulation is soy milk where whole beans can be ground and mixed with heated water to produce this consumable product. These are just a few examples of the consumable formulations that can be made from soybeans, and serve as a starting point for discussing how such formulations might be useful in human therapeutics. Oral therapies with recombinant proteins have been suggested over the years. Some advantages of such therapies include the ease of administration, patient compliance, safety, and no requirement for medical personnel, among others. Some problems with oral protein therapy include the degradation of proteins as they pass through the gastrointestinal tract and the limited bio-availability to the blood stream and other organs. Despite these limitations, the advantages of oral therapies have spawned continued interest in commercial development by attempting to overcome the shortcomings using technological advances (Karsdal et al., 2010). Theoretically, formulations made from transgenic soybean seeds expressing a particular protein may represent one technological advance that can be applied to some oral therapeutics for human or agricultural use. For discussion, consider the advantages of expressing a theoretical therapeutic protein in transgenic soybean seeds. The first consideration is purification. Bacterial, yeast, insect, mammalian, or plant culture technologies all require purification since the broth in which they propagate contain enumerable substances which must be removed before the protein can be deemed sufficiently safe to be consumed. Alternatively, the equivalent soybean-derived therapeutic protein would demand no purification. Soy formulations (e.g. soy powder, soy flour, soy milk, etc.) are consumed daily by billions of individuals and agricultural animals with few undue effects, and many health benefits (Messina, 1999; Slavin, 1991). Therefore, unless there is some compelling reason to purify a protein from soy, there would be no reason to suggest that oral therapeutic formulations made from transgenic beans would not be safe for consumption. Stated simply, purification of soybean-derived therapeutic proteins from soy is not, necessarily, a prerequisite for their use. Such a theoretical scenario becomes immediately untenable if the soybean-derived therapeutic protein is destroyed following formulation from beans to a consumable. Recent success in our laboratory (Oakes, Bost, et al., 2009), and by others (Robic, Farinas, Rech, & Miranda, 2010), has demonstrated that a variety of formulations contain intact transgenic protein. While such empirical, stability studies will need to be performed with each particular transgenic protein, there are enumerable formulation strategies which have already been industrialized for soy consumables. Gentle versus more aggressive manipulations of soybeans have already been defined for their ability to maintain endogenous protein stability. Many of these manipulations are less egregious than those used during purification of recombinant proteins from bacterial, yeast, insect, mammalian, or plant cell culture broth expression systems. With such a diversity of formulations strategies already available for soy, identifying one which maintains transgenic protein stability seems likely. While individual soybean seeds from the same and different plants can vary somewhat in the level of transgenic protein expression, this variability is not a major concern when

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developing therapeutic dosages. Soy formulations made from a lot of seeds can easily be blended into highly consistent mixtures. This is true for soy milk or soy powder formulations. Following homogenization, a given volume or weight would contain equivalent doses of the protein. Furthermore, it would be easy to standardize different lots to an equivalent dose by the addition of wild type soy milk or soy powder. Therefore the ability to formulate large lots of transgenic seeds into homogenous mixtures alleviates concerns about variability in dosing. Labile proteins which are destroyed as they pass through the gastrointestinal tract make poor oral therapeutics. However formulations made from transgenic soybeans may have some advantages in protecting the therapeutic protein while passing through the gut. Soy milk formulations have inherent buffering capacity (Lutchman et al., 2006; Park, 1991), which helps to neutralize the acidic environment in the gastrointestinal tract. Such acidneutralizing capacity of such formulations might aid in protein stability. Furthermore, current formulations made from transgenic soybean seeds would contain 1% to 4% of the therapeutic protein, leaving the remaining 96% to 99% protein as soy. These excess soy proteins in milk or powder formulations may facilitate the passage of proteins through the gut by competitively limiting protease activity. Taken together, the inherent properties of soy formulations would likely provide some protection against degradation of the therapeutic protein following oral treatment. The composition of oral therapeutics is tightly regulated to assure that no significant level of toxins, harmful substances, or transmissible agents are present. Bacterial, yeast, insect, mammalian, or plant culture technologies all require purification since the broth in which they propagate contain enumerable substances which must be removed before the protein can be deemed sufficiently safe to be consumed. Alternatively, soy protein and soy milk formulations for infant, adult, and agricultural use are safe to consume and have significant nutritional benefit (Messina, 1999; Slavin, 1991). In fact, soy formulations are so safe that they are routinely fed to infants with little side effects (Badger, Ronis, Hakkak, Rowlands, & Korourian, 2002; Motil, 2000; Seppo et al., 2005). Such widespread use suggests that therapeutic-containing soy formulations would not pose any significant risk, supporting the notion that transgenic soybean formulations would not require purification of the protein prior to treatment. While glycosylation of soybean-derived transgenic proteins will likely differ from human or animal glycosylation patterns (Liu, 1999), this too is unlikely to be problematic. Glycosylation patterns of normal soy proteins will likely be similar to those glycoslyation patterns observed on the transgenic proteins since these post translational modifications will be carried out by the same cellular machinery. If true, then humans and animals have been exposed to these particular glycosylation patterns in consumable soy formulations for a long time without undue effect. Therefore there is little reason to believe that formulations derived from transgenic soybeans will contain any harmful substances which would preclude their use in oral therapies. One recent study (Oakes, Piller, & Bost, 2009) has demonstrated the safety of oral soy formulations. While soy proteins themselves are considered safe, it was not altogether clear whether vaccine formulations which contain soy proteins combined with immune stimulating molecules would induce an anti-soy immune response. Using this mouse model, we found that no significant antibody response against soy could be detected, even when these animals were given soy protein formulations containing the oral adjuvant, cholera toxin. The likely reason for this outcome is that most animal chows contain soy protein as a significant ingredient. In fact, these mice were consumed soy in their food chow throughout

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their life, as is the case for many agricultural animals and most humans. It is likely that these mice did not respond to soy proteins since it is a normal dietary constituent. This study (Oakes, Piller, et al., 2009), therefore, demonstrated the safety of oral soy formulations, even when combined with immune stimulating agents. A final consideration for oral therapeutics is targeting the protein so that it is efficiently adsorbed from the gut, allowing therapeutic concentrations to be achieved in the target organ. There have been many strategies for targeting (Lambkin & Pinilla, 2002), including the use of fusion proteins which effectively bind particular cells and facilitate passage from the lumen or binding to a target organ. Unfortunately, fusion proteins can be large in size and complex in their folding making some systems inappropriate or incapable of expressing. As noted previously (see section 2), transgenic soybean seeds have been able to express large, complex proteins (Zeitlin, et al., 1998) which have binding characteristics. Therefore it seems likely that such fusion proteins which target cells and organs would be ideal candidates for this platform technology. Once again, there would seem to be little advantage of purifying such fusion proteins from soy formulations prior to their use since targeting would likely be unaffected by the presence of native soy proteins. In summary, we have presented the intriguing possibility that it might be possible to use formulations made from transgenic soybean seeds containing a therapeutic protein without the need for prior purification. In fact, there may even be some significant advantages of soy formulations. Taken together, an intriguing question for some soybean-derived oral protein therapeutics might be: Why purify?

8. Soybean-derived proteins: the potential for simple, low cost protein production For most therapeutic and diagnostic applications, GMP-certified proteins will require purification. Isolation schemes for such proteins can be fraught with technical challenges and regulatory oversight. While concerns about the presence of transmissible agents, trace contaminants, and necessary additives to increase protein stability continue to mount, technological advances which minimize any of these concerns would represent true breakthroughs. The use of soybean formulations to overcome present limitations, and possibly eliminate the need for purification of transgenic proteins for some applications, may represent one such solution. When considering protein purification schemes, it is useful to examine the starting material from which the protein must be isolated. For those proteins which must be isolated from human or animal tissues or fluids, concerns about transmissible agents, complexity of the starting material, and variability of purity by suppliers exist. For bacterial expression, removal of toxins, and denaturation and renaturation requirements can be costly. For yeast, insect, mammalian, or plant cell culture broths, the complexity of the starting material and the high volume to protein biomass ratios can be problematic. Regardless of the starting material or expression system, a typical isolation of a desired protein requires removal of hundreds to thousands of irrelevant proteins and similar numbers of other contaminants. Conversely, soybean seeds contain a very limited number of protein species (Mooney, Krishnan, & Thelen, 2004; Natarajan, 2010). Studies have estimated 40-50 different protein spots on 2D gel electrophoresis, with about half of these representing aggregates, subunits, or truncated forms of a few dominant soybean proteins (e.g. glycinin and conglycinin). The ability to remove such a limited spectrum of soy proteins from the desired transgenic

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protein would not seem to be a difficult task. Furthermore, these endogenous soy proteins are the same for every transgenic soybean line regardless of the foreign protein being expressed. Therefore it seems logical that once purification schemes are established for reference proteins with different physical properties (e.g. proteins with acidic versus basic isoelectric points), it will be possible to quickly adapt these standard operating procedures to new transgenic proteins. Due to the high quantity of transgenic protein per liter of soy powder (see Table 1), the starting biomass for protein purification is small and can be easily controlled. This reduces the need for concentration and further aids in the initial steps of the purification procedure. In summary, the inherent simplicity of the soybean seed and its high protein to biomass ratio should lead to the development of straightforward and cost-effective standard operating procedures for the purification of transgenic proteins.

9. Low-tech propagation and production reduces costs While technological advances have expanded the protein therapeutic and diagnostic markets, many of these achievements require significant facilities and expertise, and, therefore, increased costs. Production facilities are often highly specialized, and these buildings are expensive to maintain. Similar overhead is required for protein purification, where the low protein biomass of some expression systems dictates the quantities of material which must be processed. Contrast these requirements with those necessary for propagation of an acre of transgenic soybean plants. BSL-2 greenhouses are cheap and easy to maintain (Traynor, 2001), relative to specialized buildings for recombinant protein production. Technical support for the agricultural care of soy plants and greenhouse harvesting of beans requires substantially less training and expertise than maintaining prokaryote or eukaryote cell cultures. Furthermore, the starting material for protein purification (e.g. soy powder) is easy to obtain, and has extended pre-processing storage times. These characteristics are in stark contrast to the procedural and time constraints placed on harvesting cultured cells or media. Stocks of prokaryotic and eukaryotic cell lines expressing a particular recombinant protein must be maintained, and this low temperature storage requires significant equipment and oversight. Conversely, founder seed stocks of stably expressing transgenic soybean lines can be stored for years at room temperature or with refrigeration, and retain their ability to germinate if kept dry. This simplistic, low-tech method of maintaining stocks and lines reflects a fundamental characteristic of soybean seeds, and takes advantage of their ability to remain viable for long periods of time under ambient conditions.

10. Reduced risk of contamination with toxins and transmissible agents Regulatory agencies require lots of therapeutic and diagnostic reagent proteins to be screened for the absence of a variety of toxins and transmissible agents. These include molecules like lipopolysaccharide and other bacterial contaminants, especially when prokaryotic expression systems are being used. For proteins isolated from human or animal tissues or cell expression systems, screening for the absence of transmissible agents (e.g. HIV, Hepatitis B, etc.) is an important quality control. The number and type of screenings will likely increase as the presence of prion proteins and other agents continue to be added to the list of possible contaminants. Screening for the absence of each of these toxins or

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transmissible agents adds to the cost of production and increases possible product liability when these expression systems are used. Soybean-derived proteins pose little risk of spreading transmissible human or animal diseases. This is clear from the billions of infants, adults, and agricultural animals that routinely consume soy products. Screening for toxins or human or animal transmissible agents will likely be significantly less for lots of soybean-derived proteins. In fact, any contaminants present in soybean-derived therapeutics or diagnostics would most likely be introduced during the purification process. Ultimately, this will reduce the cost of production and limit product liability for soybean-derived proteins.

11. Waste disposal following production An increasing concern and cost-consideration for protein expression technologies is the amount and composition of waste produced during production. Regulatory requirements for decontamination and disposal of such waste into the environment add to the expense of production. This expense not only applies to the toxic byproducts produced, but to the equipment which is required. It is often more cost-efficient to replace machinery than attempt to clean and reuse it. It is highly likely that such waste disposal costs will only increase in the future. As transgenic soybean plants grow and mature, they proceed from vegetative stage to mature seed formation. As the vegetative plant dies, the seed matures, decreases its moisture content, and is then ready for harvesting. What remains after seeds have been extracted is non-germinating plant material. This material can easily be shredded, and for all intents and purposes is mulch. Stated simply, the environmental waste which requires decontamination prior to disposal for current expression systems will not be a significant cost consideration for soybean-derived proteins expressed using this platform technology.

12. Ease of scaling Transgenic soybeans as a platform technology for protein expression provide a unique opportunity for scaling production. The only real limitation for dictating the magnitude of soybean-derived protein produced is the availability of BSL-2 greenhouse space. The production facilities available for expanding other culture-based expression systems are more costly and can be specialized. Such limitations threaten to increase future costs for conventional manufacturing. Alternatively, the relative low cost of greenhouse construction and maintenance, the lack of geographical limitations for facility locations, and the low-tech requirements for production and propagation provide a compelling rational for expanding platform expression technologies to include transgenic soybeans.

13. Long term soy powder storage: Uncoupling production and purification Expiration dates for therapeutics and diagnostic reagents are required by regulatory agencies. The functional lifespan of proteins purified from any expression system depends largely upon the chemical nature of the particular protein itself, and upon the form in which that particular protein is prepared. In general, labile proteins have shorter half-lives, and lyophilized preparations have longer expiration dates than proteins in aqueous solutions. To date, these characteristics have been some of the predominant limitations that increase

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costs and require product replacement. Unfortunately, once recombinant proteins are purified, they have the half-lives and expiration dates dictated by these chemical and physical properties. Stockpiling purified proteins can be a risky proposition if such products cannot be used prior to their expiration dates. With most current expression technologies, the purification of a protein follows soon after the propagation of cells or cultures containing the desired protein. This necessitates coupling production to purification schemes in a limited time frame. Problems with production or purification can result in the lack of available product. Alternatively, an exaggerated estimate of product need may lead to overstocked, expired product. Platform expression technologies which allow the processes of production and purification to be uncoupled would provide a significant advantage. Consider the example of transgenic soybeans expressing a desired protein. A manufacturer could produce an excess of transgenic seeds and convert them to powder. At this point in production, the desired amount of soy powder could be taken for protein purification, while excess soy powder could easily be stored for months to years until needed. If there were problems with protein purification, this excess soy powder would be immediately available. If product demand had initially been underestimated, the excess powder could be used for protein purification forthwith. Cultivating an excess of transgenic soy allows stockpiling of seed material which could be used immediately or well into the future. Such flexibility adds significant value, since this lowtech, long term storage of crude product allows for future use without delay. In this manner, product expiration dates do not necessarily have to dictate production schedules.

14. Stability of transgenic proteins in seeds and soy powder: Time One function of the soybean seed is to package and store proteins. This allows seeds to be dormant for years in ambient, dry conditions and still maintain the ability to germinate. We anticipated that this fundamental property of soybean seeds would also allow transgenic proteins to be stable for years once expressed. A recent publication demonstrates that this is in fact the case for one model protein (Oakes, Bost, et al., 2009). Seeds were stored at room temperature and at ambient conditions for more than 4 years, and demonstrated no detectable degradation of the transgenic protein expressed in these seed lines. We have yet to reach a time when seed-contained transgenic proteins have demonstrated significant degradation compared to their stability in newly harvested seeds if the aged seeds are kept dry. Similar results have been obtained when analyzing the stability of soy powder formulations contaning transgenic proteins. While this first publication demonstrates the results for one model protein, we have since conducted studies in our laboratories to show stability for several additional transgenic proteins that were stored under ambient conditions for years (data not shown). Such empirical studies will need to be performed with each new protein expressed. However, the work performed to date suggests that an extended stability over time of transgenic proteins expressed in soybean seeds is a fundamental characteristic of this platform technology.

15. Stability of transgenic proteins in seeds and soy powder: Formulations In the same publication (Oakes, Bost, et al., 2009), we demonstrated the ability to formulate transgenic soybeans into milk and powders without the destruction of the expressed

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protein. Varying combinations of heating, grinding, and solvent extractions were used to demonstrate the ability to formulate this model protein without degradation or destruction. It should be noted that the processes used in this work mimicked some industry standards, suggesting that manufacturing equipment and operating procedures already in place may be adapted for use. Furthermore, we have performed similar studies with other transgenic proteins and have had success in formulating them as well while maintaining stability (data not shown). Therefore, to date, a variety of procedures which formulate soybeans into consumables did not result in the destruction of the transgenic protein. This property may not be true for every soy extraction procedure, and may not be true for every foreign protein expressed. However with the diversity of extraction methods available, it should be possible to tailor manufacturing toward optimal formulations.

16. Stability of transgenic proteins in seeds and soy powder: No cold chain requirement We have also demonstrated the ability of transgenic soy powder and milk to be shipped internationally at ambient temperatures without degradation of the protein (Oakes, Bost, et al., 2009). These studies not only demonstrate that refrigeration is unnecessary, but suggest that the process of production and protein purification can be uncoupled across continents. For example, production of transgenic seeds and processing to non-germinating powder might occur in one country. The soy powder could then be shipped internationally and stored until formulated or until the transgenic protein is isolated. Such a scenario would add manufacturing flexibility, and therefore value, for therapeutic and diagnostic proteins sent abroad. This should be especially true for emerging markets. The feasibility and success of such endeavors depends upon one fundamental property of the transgenic soybean seed; namely the ability to formulate, store, and ship material while maintaining protein stability without the requirement for cold storage.

17. A green technology Safety concerns for recombinant protein expression technologies encompass not only product liabilities, but also the environmental impact of production. Oxygen consumption and the generation of toxic byproducts are becoming increasingly scrutinized when evaluating expression platforms. Perhaps one of the most attractive attributes of transgenic soybeans as a platform for protein expression is that of being a green technology. Growth of these plants consumes carbon dioxide and releases oxygen, which contrasts with most other prokaryotic and eukaryotic cell culture systems. Thus, the environmental-friendly production of proteins using this green technology is another inherent advantage of transgenic soybeans.

18. Future directions Translation of transgenic soybeans as a platform technology toward commercialization is developing; however there are several hurdles which must be overcome. Standard operating procedures for growth, formulation, purification, and storage of soybean-derived powder, milk and purified proteins using GMP that are consistent with regulatory agency standards must be delineated. In addition, product data for stability, expiration dates, purity, content

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and substantial equivalence must also be defined. Future success with these endeavors will be required to move this novel platform technology toward market acceptance, and ultimately to product commercialization.

19. Conclusion Transgenic soybean seeds represent a unique platform for the expression and accumulation of a desired protein. The advantages of this protein expression system stem from basic seed biology and their ability to express high levels of glycosylated protein for storage over time. For those applications which require high protein expression in a form that is stable in the environment at very low cost, transgenic soybean seeds provide significant advantages. As protein expression technologies expand and evolve, a consideration of the optimal platform for each protein produced seems sensible. Transgenic soybean seeds represent an emerging technology with significant advantages for expressing some recombinant proteins.

20. References Badger, T. M., Ronis, M. J., Hakkak, R., Rowlands, J. C., & Korourian, S. (2002). The health consequences of early soy consumption. J Nutr, 132(3), 559S-565S. Brondyk, W. H. (2009). Selecting an appropriate method for expressing a recombinant protein. Methods Enzymol, 463, 131-147. Cregg, J. M., Tolstorukov, I., Kusari, A., Sunga, J., Madden, K., & Chappell, T. (2009). Expression in the yeast Pichia pastoris. Methods Enzymol, 463, 169-189. Cunha, N. B., Araujo, A. C., Leite, A., Murad, A. M., Vianna, G. R., & Rech, E. L. (2010). Correct targeting of proinsulin in protein storage vacuoles of transgenic soybean seeds. Genet Mol Res, 9(2), 1163-1170. Ding, S. H., Huang, L. Y., Wang, Y. D., Sun, H. C., & Xiang, Z. H. (2006). High-level expression of basic fibroblast growth factor in transgenic soybean seeds and characterization of its biological activity. Biotechnol Lett, 28(12), 869-875. Garg, R., Tolbert, M., Oakes, J. L., Clemente, T. E., Bost, K. L., & Piller, K. J. (2007). Chloroplast targeting of FanC, the major antigenic subunit of Escherichia coli K99 fimbriae, in transgenic soybean. Plant Cell Rep, 26(7), 1011-1023. Geisse, S., & Fux, C. (2009). Recombinant protein production by transient gene transfer into Mammalian cells. Methods Enzymol, 463, 223-238. Hellwig, S., Drossard, J., Twyman, R. M., & Fischer, R. (2004). Plant cell cultures for the production of recombinant proteins. Nat Biotechnol, 22(11), 1415-1422. Hyten, D. L., Cannon, S. B., Song, Q., Weeks, N., Fickus, E. W., Shoemaker, R. C., et al. (2010). High-throughput SNP discovery through deep resequencing of a reduced representation library to anchor and orient scaffolds in the soybean whole genome sequence. BMC Genomics, 11, 38. Jarvis, D. L. (2009). Baculovirus-insect cell expression systems. Methods Enzymol, 463, 191222. Karsdal, M. A., Henriksen, K., Bay-Jensen, A. C., Molloy, B., Arnold, M., John, M. R., et al. (2010). Lessons Learned From the Development of Oral Calcitonin: The First Tablet Formulation of a Protein in Phase III Clinical Trials. J Clin Pharmacol. Lambkin, I., & Pinilla, C. (2002). Targeting approaches to oral drug delivery. Expert Opin Biol Ther, 2(1), 67-73.

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Lienard, D., Sourrouille, C., Gomord, V., & Faye, L. (2007). Pharming and transgenic plants. Biotechnol Annu Rev, 13, 115-147. Liu, K. (1999). Soybeans: Chemistry, Technology, and Utilization. Gaithersburg, MD: Aspen Publishers, Inc. Lutchman, D., Pillay, S., Naidoo, R., Shangase, N., Nayak, R., & Rughoobeer, A. (2006). Evaluation of the buffering capacity of powdered cow's, goat's and soy milk and non-prescription antacids in the treatment of non-ulcer dyspepsia. S Afr Med J, 96(1), 57-61. McPherson, R. M., & MacRae, T. C. (2009). Evaluation of transgenic soybean exhibiting high expression of a synthetic Bacillus thuringiensis cry1A transgene for suppressing lepidopteran population densities and crop injury. J Econ Entomol, 102(4), 16401648. Messina, M. J. (1999). Legumes and soybeans: overview of their nutritional profiles and health effects. Am J Clin Nutr, 70(3 Suppl), 439S-450S. Mooney, B. P., Krishnan, H. B., & Thelen, J. J. (2004). High-throughput peptide mass fingerprinting of soybean seed proteins: automated workflow and utility of UniGene expressed sequence tag databases for protein identification. Phytochemistry, 65(12), 1733-1744. Moravec, T., Schmidt, M. A., Herman, E. M., & Woodford-Thomas, T. (2007). Production of Escherichia coli heat labile toxin (LT) B subunit in soybean seed and analysis of its immunogenicity as an oral vaccine. Vaccine, 25(9), 1647-1657. Motil, K. J. (2000). Infant feeding: a critical look at infant formulas. Curr Opin Pediatr, 12(5), 469-476. Natarajan, S. S. (2010). Natural variability in abundance of prevalent soybean proteins. Regul Toxicol Pharmacol. Oakes, J. L., Bost, K. L., & Piller, K. J. (2009). Stability of a soybean seed-derived vaccine antigen following long-term storage, processing and transport in the absence of a cold chain. Journal of the Science of Food and Agriculture, 89(13), 2191-2199. Oakes, J. L., Piller, K. J., & Bost, K. L. (2009). An antibody response to cholera toxin, but not soy proteins, following oral administration of adjuvanted soybean formulations. Food and Agricultural Immunology, 20(4), 305-317. Padgette, S. R., Kolacz, K. H., Delannay, X., Re, D. B., Lavallee, B. J., Tinius, C. N., et al. (1995). Development, Identification, and Characterization of a Glyphosate-Tolerant Soybean Line. Crop Science, 35(5), 1451-1461. Park, Y. W. (1991). Relative buffering capacity of goat milk, cow milk, soy-based infant formulas and commercial nonprescription antacid drugs. J Dairy Sci, 74(10), 33263333. Piller, K. J., Clemente, T. E., Jun, S. M., Petty, C. C., Sato, S., Pascual, D. W., et al. (2005). Expression and immunogenicity of an Escherichia coli K99 fimbriae subunit antigen in soybean. Planta, 222(1), 6-18. Qi, Q., Huang, J., Crowley, J., Ruschke, L., Goldman, B. S., Wen, L., et al. (2010). Metabolically engineered soybean seed with enhanced threonine levels: biochemical characterization and seed-specific expression of lysine-insensitive variants of aspartate kinases from the enteric bacterium Xenorhabdus bovienii. Plant Biotechnol J.

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Rao, S. S., & Hildebrand, D. (2009). Changes in oil content of transgenic soybeans expressing the yeast SLC1 gene. Lipids, 44(10), 945-951. Robic, G., Farinas, C. S., Rech, E. L., & Miranda, E. A. (2010). Transgenic soybean seed as protein expression system: aqueous extraction of recombinant beta-glucuronidase. Appl Biochem Biotechnol, 160(4), 1157-1167. Schmidt, M. A., & Herman, E. M. (2008). Proteome rebalancing in soybean seeds can be exploited to enhance foreign protein accumulation. Plant Biotechnol J, 6(8), 832-842. Schmutz, J., Cannon, S. B., Schlueter, J., Ma, J., Mitros, T., Nelson, W., et al. (2010). Genome sequence of the palaeopolyploid soybean. Nature, 463(7278), 178-183. Seppo, L., Korpela, R., Lonnerdal, B., Metsaniitty, L., Juntunen-Backman, K., Klemola, T., et al. (2005). A follow-up study of nutrient intake, nutritional status, and growth in infants with cow milk allergy fed either a soy formula or an extensively hydrolyzed whey formula. Am J Clin Nutr, 82(1), 140-145. Slavin, J. (1991). Nutritional benefits of soy protein and soy fiber. J Am Diet Assoc, 91(7), 816819. Traynor, P. L., Adair, D., Irwin, R. (2001). A practical guide to containment: Greenhouse research with transgenic plants and microbes. Blacksburg, VA: Information Systems for Biotechnology. Wang, X., Wang, Y., Tian, J., Lim, B. L., Yan, X., & Liao, H. (2009). Overexpressing AtPAP15 enhances phosphorus efficiency in soybean. Plant Physiol, 151(1), 233-240. Zeitlin, L., Olmsted, S. S., Moench, T. R., Co, M. S., Martinell, B. J., Paradkar, V. M., et al. (1998). A humanized monoclonal antibody produced in transgenic plants for immunoprotection of the vagina against genital herpes. Nat Biotechnol, 16(13), 13611364. Zerbs, S., Frank, A. M., & Collart, F. R. (2009). Bacterial systems for production of heterologous proteins. Methods Enzymol, 463, 149-168.

2 Optimizing Recombinant Protein Expression in Soybean Laura C. Hudson, Kenneth L. Bost and Kenneth J. Piller University of North Carolina at Charlotte and SoyMeds, Inc. United States of America 1. Introduction The production of biopharmaceuticals represents the fastest growing segment in the pharmaceutical industry. Currently, the majority of biopharmaceuticals are produced in recombinant microbe expression systems. However, microbes have certain limitations in the classes of proteins that can be economically produced, and in the post-translational processing that can be achieved. To solve this problem, insect and mammalian cell cultures have also been utilized for eukaryotic protein production (Lubiniecki and Lupker, 1994; Kost and Condreay, 1999). However, a significant problem with these systems, in particular cell cultures, is that production costs are prohibitively high for many proteins. Therefore, an increased demand for biopharmaceuticals will require improved and cost effective manufacturing practices as well as practical transportation and delivery methods for a global community. Over the past two decades, there has been substantial research on the expression of heterologous proteins in plants as a means to produce biopharmaceuticals that can meet current and future global needs. Several excellent review articles describe these recent advances (Streatfield 2007; Karg and Kallio 2009; Franconi et al., 2010). While numerous plant systems have been shown to support expression of heterologous proteins, we believe that soybean is perhaps the most practical of these systems. Soybean is often overlooked as an expression system in part due to a technically challenging, lengthy, and costly transformation process. However, there is enormous potential for the use of transgenic soybean as a factory for the economic production of pharmaceutical proteins. The soybean system has distinct advantages which offer a practical alternative to existing expression systems. First, while soybeans are traditionally thought of as high oil seeds, they are also high protein seeds. Soybeans contain nearly 40% protein by dry mass, and represent one of the richest natural sources of protein known. Given this high protein content, it is therefore possible to express in excess of a milligram of transgenic protein in a single soybean seed. There are few, if any, host systems (plant or non-plant) that can produce such levels of foreign protein based on weight. Second, soybean is a relatively easy and inexpensive plant to grow. Therefore, the production of biopharmaceuticals in soybeans is extremely cost-effective. For example, given the high protein content of seeds and a foreign protein expression level of 1%-4% of total soluble protein (TSP), large greenhouses could produce millions of doses of a vaccine for pennies per dose. Also contributing to the low cost of heterologous protein expression in soy are the well-known and established procedures for processing soybeans

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into palatable products. Lastly, soybeans are safe and do not pose significant risk for humans or livestock since soy is a major food staple and an integral component of most diets. Numerous products containing soy are currently consumed by a large number of humans and animals across the globe (Lusas and Riaz, 1995). Soybean based pharmaceuticals could become extremely beneficial in developing countries. The World Health Organization estimates that nearly 3 million people die each year from vaccine preventable disease (Thomson 2006). In developing countries, people cannot afford the high cost of vaccines, but the use of cost-effective, soy-derived pharmaceuticals could address this issue. Also, in developing areas there is often a lack of infrastructure, including access to refrigeration, making it difficult to transport and store pharmaceuticals. Soy-based pharmaceuticals, whether shipped as processed powder or as a soymilk formulation, would have the potential to eliminate the current requirement for a cold chain and therefore offer practical alternatives. With soymilk formulations, this is possible because formulations could be lyophilized or dehydrated in a manner similar to that for making powdered dry milk, and then packaged, shipped, and stored without a requirement for a cold chain. Aliquoted doses could then be reconstituted on site and administered when needed. As a plant, soybean offers another advantage over the use of microbial systems in that they possess the necessary machinery for generating large and complex proteins. Plant cells are capable of all eukaryotic post-translational modifications (Hood et al., 1997), including disulfide bond formation, glycosylation, and subunit protein assembly. Often, these posttranslational processes do not have the drawbacks associated with other expression systems. For example, glycosylation in plant systems can be achieved without the hyperglycosylation observed in other systems such as yeast (Nakamura et al., 1993; Karnoup et al., 2005). Furthermore, plant cells do not harbor human or zoonotic pathogens, making them a safe host for the production of pharmaceuticals for both human and agricultural use. Finally, the concept of expressing a foreign protein in soybean has been proven by several groups who have established and attained feasible levels of protein expression (Piller et al., 2005 ; Ding et al., 2006 ; Morevec et al., 2007 ; Garg et al., 2007 ; Schmidt et al., 2008). In this chapter, we will focus on incorporating our current knowledge for optimizing expression in soybeans and to evaluate this unique host as a platform for the expression of a wide variety of pharmaceutical proteins such as vaccine subunits.

2. Selection of a practical promoter to drive transgene expression A critical first step for efficient production of heterologous protein in any recombinant system is to maximize the levels of foreign protein expression, as doing so will ultimately decrease production costs. However, reaching high levels of expression is a key limiting factor in the production of foreign proteins in a host system. Since transcription is one of the earliest steps in the process of protein production, it is an obvious place to focus when attempting to increase recombinant protein yield. To increase transcription levels, it is important to choose a promoter that can drive optimal transcription of a desired protein. Promoter sequences must be added for genes to be correctly translated into proteins, and because of this critical regulatory activity, promoters have been studied extensively in efforts to improve the efficiency of expression in transgenic host systems. To date, some of the most common promoters used to express foreign proteins in transgenic crops have been constitutive in nature. Constitutive promoters drive gene expression in the majority of plant tissue types as well as throughout all life stages of plant, flower, and seed

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maturation. One of the most common constitutive promoters used for plant biotechnology applications is the cauliflower mosaic virus 35S promoter (35S CaMV; Odell et al., 1985) which has been shown to result in high levels of recombinant protein expression (Fiedler et al., 1997; Gutierrez-Ortega et al., 2005). While constitutive promoters have many advantages, there are also potential negative factors associated with these promoters that should be considered when designing a gene expression cassette. For example, constitutive promoters can consume unnecessary energy and resources, and can interfere with important agronomic factors such as growth habit and the ability to photosynthesize. Furthermore, expression in unwanted locations may require additional biohazard disposal (e.g. to remove unwanted parts of the plant) or containment (e.g. to prevent out crossing from pollen, etc.), both of which could negatively impact the cost-effectiveness of the system. In contrast, tissue-specific promoters express spatially and temporally, allowing foreign proteins to be expressed in a desired location or at a desired point in the host life cycle. Seed-specific promoters are one example of tissue-specific promoters that can be used to target expression to different stages of seed development. Typical seed-specific promoters used for heterologous expression in soybean include the ß-conglycincin (7S) and glycinin (11S) promoters (Eckert et al., 2006; Nielsen et al., 1989). Several groups have used these promoters to successfully express recombinant proteins in soybean seeds (Ding et al., 2006; Moravec et al., 2007). While constitutive promoters can also drive expression in seeds (Zeitlin et al., 1998, Piller et al., 2005), it is likely that higher levels of expression in seeds can be achieved using seed-specific promoters.

3. Molecular optimization of synthetic genes There are many challenges associated with expressing a functional protein outside of its native environment. The genetic information encoded in an open reading frame goes far beyond simply stating the order of amino acids in a protein. For example, different organisms prefer different codons when making a functional protein. The genetic code is comprised of 64 codons which encode 20 structural amino acids and three stop codons to terminate translation. Each codon is read in the ribosome by complementary tRNAs that transfer specific amino acids to a growing polypeptide chain. The overabundance in the number of codons allows many amino acids to be encoded by more than one codon. Different organisms often show particular preferences for one of the several codons that encode for the same amino acid. This phenomenon, called codon bias, has been identified as an important factor in gene expression. The degree to which any given codon appears in the genetic code can vary significantly between organisms. This is due to the fact that preferred codons correlate with the abundance of cognate tRNAs available within the cell. When a foreign gene is expressed in a non-native host, sequences that may contain expressionlimiting factors and low-frequency codons in the host should be eliminated. There is a definite correlation between the frequency of rare codons and the level of foreign protein expression. Thus, it is generally understood that rare codons, especially at the N-terminus of a protein, should be avoided in the design of heterologous genes. Current improvements in the cost and speed of gene synthesis can facilitate the complete redesign of an entire gene sequence to maximize the likelihood of high protein expression. In this case, a gene of interest can be synthesized by replacing infrequently used condons with those that are preferred by soybean without changing the amino acid sequence.

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Codon tables are available for most organisms. Such tables are compiled by tabulating the total number of codons present in all known genes for a particular organism, and then showing totals as a percentage or frequency. While the majority of available codon tables represent codon frequency of all genes within a particular organism, custom codon tables can be generated to represent codon frequency within a specific organ type. For example, Figure 1 shows a custom codon table that our laboratory generated for heterologous protein expression in soybeans. Since the majority of soybean protein is comprised of 7S and 11S storage protein, and our laboratory utilizes the soybean 7S and 11S promoters to target expression to seeds, we chose the eight most abundant proteins belonging to the 7S and 11S gene families to comprise a codon table based on these gene sequences. Thus, the custom table in Figure 1 reflects the codon bias of proteins residing in the environment that we are targeting for expression.

Fig. 1. Codon Bias Table for Glycine Max (soybean) seeds. This custom table was assembled from codons present in highly expressed soybean 11S protein (G1, G2, G3, G4, G5) and 7S protein (α, α′and β subunits of β-conglycinin). It is generally understood that the use of low frequency codons (e.g. those used 1% of total soluble protein in T1 seeds. Visualization of the recombinant protein was carried out using western blot analyses. The predicted molecular mass of synthetic mutant SEB is ~29 kDa, and this is the precise size of transgenic protein detected following SDS-PAGE and western analysis (Figure 6). It is important to note that there is only one single band diagnostic of the recombinant mSEB protein, and the mobility of this novel protein is consistent with the predicted size of mature SEB (~29 kDa) suggesting that soy-derived mSEB was correctly processed. It is important to note that while both gene cassettes predicted pre-proteins with different mobilities (due to

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Fig. 6. Western blot analysis showing protein expression of 7S-mSEB and Gly-mSEB. Bacterially-derived mutant SEB (rSEB) served as a positive control and relative quantification standard. Amounts represent total soluable protein of seed. the length of the signal peptide), the detected proteins showed similar mobility when separated in SDS-PAGE experiments. If correct processing of the N-terminus had not occurred, the putative signal peptide would still be attached to the N-terminus resulting in molecules showing altered motility when separated in SDS-PAGE gels. Furthermore, bacterially-expressed recombinant mutnat SEB (rSEB) containing a histidine tag (GGHHHHHH) was included as a positive control. The rSEB is predicted to migrate slightly slower than soy-derived mSEB due to the presence of the histidine tag at the C-terminus. This difference was also detected in western analyses (Figure 6). While these data suggest that both soy-derived proteins were correctly processed, definitive conclusions can only be drawn following analytical characterization of the N-termini. In this respect, N-terminal sequencing was performed on mSEB isolated from T1 seeds transformed with the 7S-mSEB cassette. Results from this experiment verified the composition of 11 amino acids present at the N-terminus which were identical to those reported in the GenBank database (K. Piller, unpublished results). N-terminal sequencing is currently in progress to identify terminal residues in the glycinin-mSEB fusion protein. We also visualized localization of the soy-derived mSEB proteins under control of the two different promoters and signal peptides using immunohistochemistry and confocal microscopy. Seed coats were removed and cotyledons were fixed in formaldehyde and embedded in paraffin. The tissue was incubated with a rabbit anti-SEB antibody followed by an Alexaflour488 goat anti-rabbit IgG-HRP conjugated secondary antibody. Images were collected with a LSM 710 Spectral Confocor 3 Confocal Microscope using a 20X objective and a 405nm laser to visualize DAPI stained nuclei along with a 488nm laser to collect emitted fluorescence. Figure 7A shows that mSEB protein derived from the 7S promoter and native SEB signal peptide construct was not retained within the plant cell and was instead secreted into the apoplastic spaces between cells. The localization of mSEB to apoplastic spaces was anticipated. Figure 7B shows a wild type control seed section for comparison. The mSEB protein derived from seeds transformed with the glycinin promoter and glycinin signal peptide, in contrast, remained intracellular and was localized throughout the cytoplasm as shown in Figure 7C, with a wild type control in Figure 7D. While this result

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was not completely surprising, it suggested that amino acid sequences present within the glycinin signal peptide may have contained “address tags” which dictated final localization (Mølhøj and Del Degan, 2004). Collectively, these data show the utility of signal peptides and prediction software to aid in the design of gene cassettes targeting expression to subcellular locations. In the case of mSEB, we found that a native bacterial sequence and a known soybean signal peptide both functioned as signal peptides in vivo. Furthermore, both gene constructs allowed mSEB to accumulate to levels representing >2% seed TSP. While relatively small numbers of transformed lines (2-fold increases in mSEB expression compared with the T1 parent. While a 2-fold increase in expression correlates with doubling in copy number, it is not clear why two to four-fold expression increases are often detected. One explanation for this observation is that a transgene requires a full generation to “re-set” itself while another could have to do with differences in seed quality. While a two to four-fold increase in protein expression is welcome, the converse is observed and heterologous protein expression decreases between the T1 and T2 generations. One such example is shown in Figure 9B. In this experiment, equal amounts of seed extract from a T1 parent and four individual T2 progeny were also characterized by western analysis. Several factors could contribute to decrease in protein expression. For example, multiple copies of the gene cassette may have been inserted during the transformation process, and these additional copies may be responsible for gene silencing and an associated decrease in protein expression. While we observe decreases in expression such as those shown in Figure 8B, they are not as common as events which show increases in expression such as those in Figure 8A. Therefore, when possible, we prefer to quantify expression once homozygosity is achieved and recombinant protein production has had a chance to stabilize.

9. Simple methods for purification of foreign proteins Until this point the current chapter has mainly focused on strategies to achieve optimal expression of foreign proteins using soybean as an expression platform. Given the high protein content of soybeans, and expression levels typically between 1% and 4% of total soluble seed protein, the generation of milligram quantities of transgenic protein is usually attainable. In those cases fractionation or purification of the desired protein is not necessary. However, in cases where foreign proteins are not expressed at desired levels despite exhaustive attempts using known methodologies to increase expression, there are simple time and cost-effective methods that can be employed to generate a partially purified and more concentrated product. Any given protein may represent a small or large amount of the total protein composition within a cell. These proteins can be soluble or insoluble, membrane-bound, DNA-bound, cytoplasmic, or localized within organelles. If purification of a heterologous protein is needed, the challenge would be to separate that protein from all other components in the cell with reasonable efficiency, speed, yield, and purity, while still maintaining the biological activity and chemical integrity of the polypeptide. Proteins vary in a number of their physical and chemical properties due to size and amino acid composition. Amino acid residues may be positively or negatively charged, neutral and polar, or neutral and hydrophobic. Proteins also have very definite secondary and tertiary structures which create a unique size, shape, and distribution of residues on their surface. It is possible to exploit the differences in properties between the protein of interest and other host proteins to establish partial purification of a heterologous protein. One simple and cost-effective way to partially purify a heterologous protein is to take advantage of its unique isoelectric point (pI). The pI of a protein is the specific pH at which that protein has no net electrical charge. The pI affects the solubility of a protein at a given pH, with minimum solubility in solutions with a pH corresponding to their pI value. Thus, a protein usually remains soluble in a solution when the pH is either higher (net negative charge) or lower (net positive charge) than its pI value. The majority of soluble protein in soymilk is generally classified as 11S protein, 7S protein, and whey protein (Nielsen et al., 1989). Soymilk proteins within each of these three classes

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have relatively low pIs. Altering the pH of a soymilk solution, therefore, allows for a relatively simple method to precipitate major classes of proteins (Thanh and Shibasaki 1976). For example, decreasing the pH of a soymilk solution to 6.4 will precipitate proteins in the 11S family while further decreasing the pH to 4.8 will precipitate proteins in the 7S family. Thus, if the isoelectric point of a heterologous protein is greater than pH 6.4, isoelectric fractionation can be used to remove 11S and 7S proteins which comprise the majority of protein in soymilk solutions. To demonstrate the utility of isoelectric fractionation, we prepared soymilk from transgenic seeds expressing the mSEB protein. There are several software programs that can be used to predict the pI of protein based on known amino acid sequence. One of these programs was used to predict a pI 8.52 for mSEB. Based on this pI value, we anticipated that soy-derived mSEB would remain soluble in a soymilk solution as the pH of the milk was lowered, allowing precipitation of 11S and 7S proteins. To test our hypothesis, soymilk was prepared in a 1X PBS solution. The pH of the starting soymilk solution was 7.8. A dilute solution of HCL was added dropwise to lower the pH of the soymilk to pH 6.8. At this point, the milk solution became opaque, presumably due to the precipitation of 11S proteins. The precipitated milk proteins were removed from solution by centrifugation. The dilute HCL solution was used to further decrease the pH of the milk to 4.8. The decrease from pH 6.4 to 4.8 again resulted in an opaque-colored solution. The precipitated milk proteins from the second pH drop were also removed by sedimentation, and both protein pellets were resuspended in 1X PBS (pH 7.4). Figure 10A shows a schematic of the process described above. Samples of the starting material, as well as soluble and insoluble fractions from each pH drop were subjected to SDS-PAGE and western analysis. The results of this experiment are shown in Figure 10B.

Fig. 10. Partial purification of mSEB using isoelectric fractionaton. A. Schematic of soybean protein families that precipitate at various pH. B. Western blot (top panel) and coomassiestained membrane (bottom panel) of soymilk protein (20ug) following isoelectric fractionation.

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As predicted, soy-derived mSEB remained soluble with 7S and whey proteins following initial precipitation and sedimentation of the 11S proteins. Further separation of that mixture revealed fractionation of mSEB with whey protein, while 7S proteins precipitated at pH 4.8. It should be noted that the relative amount of mSEB did not decrease during the fractionation, nor did it appear to lose immunogenicicty as detected by primary antibody in the western gel experiments. This observation was important because it confirmed the stability and immunogenicity of the end-point product. Membranes were stained with Coomassie blue dye (Figure 10C) to visually observe the composition of proteins following fractionation. Based on the known amounts of protein loaded in each lane, as well as the relative amount of mSEB detected in the western, we were able to achieve a four-fold concentration of this model heterologous protein using isoelectric fractionation. The above results demonstrate that partial purification of a heterologous protein can be accomplished using adjustments to pH in a soymilk formulation. This experiment took only about one hour to perform, and resulted in a four-fold purification with little detectable loss of immunogenicity. In theory, if additional concentration (with respect to volume) was needed then fractionated soymilk solutions could undergo partial lyophilization to further increase heterologous protein concentration. A five-fold reduction in volume, coupled with a four-fold increase in heterologous protein accumulation would result in a 20-fold increase in heterologous protein concentration. Such concentrations may be beneficial when attempting to address basic scientific questions with heterologous proteins that express at sub-optimal levels. Our laboratory has used the above methods to concentrate a soymilk vaccine formulation prior to efficacy testing in mice when limitations were placed on the volume of material that could be gavaged. While the above example demonstrated fractionation of a heterologous protein with a relatively high pI, it should be noted that this simple methodology could also be used to effectively precipitate heterologous proteins with any pI value. In this manner, the heterologous protein would precipitate with host proteins sharing the same relative pI values. Our laboratory has also used isoelectric fractionation to demonstrate that a protein with a predicted pI of 5.5 could be precipitated from soymilk solutions following a drop in pH, and then reconstituted and used for downstream analyses (data not shown). While isoelectric fractionation can be applied to any host system, the physical properties of major soy protein families provide an advantage for fractionation of proteins expressed in this host.

10. Conclusion Soybeans have the ability to assemble and accumulate many valuable proteins that can be used for the production of pharmaceuticals. This host system offers many advantages that are not present in other host systems, and shows great potential to offer safe and costeffective protein-based products that can be used worldwide. In this chapter, we have discussed our knowledge for optimizing recombinant protein expression in soybeans. In our efforts to optimize the production of these proteins we have used codon optimization for expression in soybean. We have compared targeted expression to the whole plant and chloroplasts using constitutive promoters as well as to seeds using different seed-specific promoters. We have also analyzed the effects of a variety of plant and non-plant based signal peptides to target proteins to the secretory pathway of cells. We have also shown that protein expression levels can fluctuate until homozygosity and protein stabilization has

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been achieved. Protein expression levels based on the use of various combinations of promoters, leader sequences, and signal peptides resulted in protein expression that typically approached 2%-4% of total soluble protein extracted from seeds. While the optimal destination for recombinant proteins may need to be determined empirically, our results demonstrate that targeting proteins to the soybean seed through the secretory pathway resulted in the highest levels of recombinant protein accumulation.

12. References Andrews L.B., Curtis W.R. (2005). Comparison of transient protein expression in tobacco leaves and plant suspension culture. Biotechnol Prog., 21(3), 945-952. Bhattacharyya, M.K., Stermer, B.A., Dixon, R.A. (1994). Reduced variation in transgene expression from a binary vector with selectable markers at the right and left T-DNA borders. The Plant Journal, 6(6), 957-968. Caligari, P.D.S., Yapabandara, Y.M.H.B., Paul, E.M., Perret, J., Roger, P., Dunwell, J.M. (1993). Field performance of derived generations of transgenic tobacco. Tag Theoretical and Applied Genetics, 86(7), 875-879. Chalfie M., Tu Y., Euskirchen G., Ward W., Prasher D. (1994). Green fluorescent protein as a marker for gene expression. Science 263, (5148), 802–805. Chyi, Y., Jorgensen, RA., Goldstein, D., Tanksley, S.D., Loaiza-Figueroa, F. (1986). Locations and stability of Agrobacterium-mediated T-DNA insertions in the Lycopersicon genome. Molecular and General Genetics MGG, 204(1), 64-69. De Angioletti M., Lacerra G., Sabato V., Carestia C. (2004). Beta+45 G --> C: a novel silent beta-thalassaemia mutation, the first in the Kozak sequence. Br J Haematol, 12,(2), 224–231. Ding, S.H., Huang, L.Y., Wang, Y.D., Sun, H.C., & Xiang, Z. H. (2006). High-level expression of basic fibroblast growth factor in transgenic soybean seeds and characterization of its biological activity. Biotechnol Lett, 28(12), 869-875. Eckert H., LaVallee B.J., Schweiger B.J., Kinney A.J., Cahoon E.B., Clemente T. (2006). Coexpression of the borage Δ6 desaturase and the Arabidopsis Δ15 desaturase results in high accumulation of stearidonic acid in the seeds of transgenic soybean. Planta, 224, 1050-1057. Fiedler U., Phillips J., Artsaenko O., Conrad U. (1997). Optimization of scFv antibody production in transgenic plants. Immunotechnology, 3, 205-216. Franconi R., Demurtas O.C., Massa S. (2010). Plant-derived vaccines and other therapeutics produced in contained systems.Expert Rev Vaccines, 9(8), 877-892. Gallie D.R., Walbot V. (1992). Identification of the motifs within the tobacco mosaic virus 5′leader responsible for enhancing translation. Nucleic Acids Res.,20, 4631–4638. Garg, R., Tolbert, M., Oakes, J.L., Clemente, T.E., Bost, K.L., & Piller, K.J. (2007). Chloroplast targeting of FanC, the major antigenic subunit of Escherichia coli K99 fimbriae, in transgenic soybean. Plant Cell Rep, 26(7), 1011-1023. Gils M., Kandzia R., Marillonnet S., Klimyuk V., Gleba Y. (2005). High-yield production of authentic human growth hormone using a plant virus-based expression system. Plant Biotechnol J, 3(6), 613-620. Gutierrez-Ortega A., Sandoval-Montes C., de Olivera-Flores T.J., Santos-Argumedo L., Gomez-Lim, M.A.(2005).Expression of a functional interleukin—12 from mouse in transgenic tomato plants. Transgenic Res,14, 877-885. Hood E.A., Witcher D.P., Maddock S., Meyer T., Baszczynski C., Bailey M. , Flynn P., Register J., Marshall L. Bond D., et al. (1997). Commercial production of avidin

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Nielsen N..C., Dickinson C. D, Cho T. J., Thanh V. H., Scallon B. J., Fischer R. L., Sims T. L., Drews G. N., R. B. Goldberg R. B.(1989). Characterization of the Glycinin Gene Family in Soybean. The Plant CellL, 1, (3) 313-328. Odell J.T.,Nagy F.,Chua N.H. (1985). Identification of DNA sequences required for activity of the cauliflower mosaic virus 35S promoter. Nature, 313, 810–812. Padidam, M.; Cao, Y. J. (2001). Elimination of transcriptional interference between tandem genes in plant cells. BioTechniques, 31,328–334. Piller, K. J., Clemente, T.E., Jun, S.M., Petty, C.C., Sato, S., Pascual, D.W., et al. (2005). Expression and immunogenicity of an Escherichia coli K99 fimbriae subunit antigen in soybean. Planta, 222(1), 6-18. Puonti-Kaerlas, J., Eriksson, T., Engstrom, P. (1992). Inheritance of a bacterial hygromycin phosphotransferase gene in the progeny of primary trangenic pea plants. Tag Theoretical and Applied Genetics, 84(3-4), 443-450. Schillberg S., Zimmermann S., Voss A., Fisher R. (1999). Apoplastic and cytosolic expression of full size antibodies and antibody fragments in Nicotiana tabacum. Transgenic Research, 8 (4), 255-263. Schmidt, M.A., and Herman, E.M. (2008). Proteome rebalancing in soybean seeds can be exploited to enhance foreign protein accumulation. Plant Biotechnol J, 6, 832-842. Spencer, T. M., O’Brien, J. V., Start, W. G., Adams, T. R., Gordon-Kamm, W. J., Lemaux, P. G. (1992). Segregation of transgenes in maize. Plant Molecular Biology, 18(2), 201-210. Streatfield S.J., Lane R.L., Brooks A.K, Barker D.K, Poage M. L., Mayor J.M., Lamphear B.J., Drees C.F. Jilka J.M., Hood E.E., Howard J.A. (2003). Corn as a production system for human and animal vaccines. Vaccine, 21, 812-815. Streatfield S.J. (2007). Approaches to achieve high-level heterologous protein production in plants. Plant Biotechnol J, 5(1), 2-15. Thanh V.H. & Shibasaki K. (1976). Major protein of soybean seeds: a straightforward fractionation and their characterization. J Agric Food Chem., 24, 1117-1121. Thomson JA. (2006). Seeds for the future: The impact of genetically modified crops on the environment. Australia, Cornell University Press. Thompson A.J. and Myatt S.C. (1997).Tetracycline-dependent activation of an upstream promoter reveals transcriptional interference between tandem genes within T-DNA in tomato. Plant Molecular Biology, 34, 687–692. Walters, D.A., Vetsch, C.S., Potts, D.E., Lundquist, R.C. (1992). Transformation and inheritance of a hygromycin phosphotransferase gene in maize plants. Plant Molecular Biology, 18(2), 189-200. Zeitlin L., Olmsted S.S., Moench T.R., Co M.S. (1998). A humanized monoclonal antibody produced in transgenic plants for immunoprotection of the vagina against genital herpes. Nature Biotech, 11(1), 1361-1364. Zhong G.Y., Peterson D., Delaney D.E., Bailey M., Witcher D.R., Register III J.C., Bond D. , Lin C.P., Marshall L. , Kulisek E., Ritland D., Meyer D., Hood E.E. , Howard J.J. (1999). Commercial production of aprotinin in transgenic maize seeds. Molecular Breeding, 5, 345-356. Zimmermann S., Schillberg S., Liao Y., Fisher R. (1998). Intracellular expression of TMVspecific single-chain Fv fragments leads to improved virus resistance in shape Nicotiana tabacum. Mol Breeding, 4(4), 369–379.

3 Virus-Induced Gene Silencing of Endogenous Genes and Promotion of Flowering in Soybean by Apple latent spherical virus-Based Vectors Noriko Yamagishi and Nobuyuki Yoshikawa Iwate University Japan 1. Introduction Soybean, which has been traditional food in Asian countries, is one of the most important crops due to its high quality protein and oil. Various functional components derived from secondary metabolite also have received significant attention in terms of human health. Elucidation of the gene function associated with the biosynthesis of various seed components at molecular level will provide valuable information for improvement of soybean. A great deal of information at the molecular level of soybean, including expression sequence tag (EST) libraries, microarray data, genome sequences, genetic linkage map, comparative genomics, and DNA markers has been reported (Alkharouf et al., 2004; Cheng et al., 2008; Choi et al., 2007; Grant et al., 2010; Haerizadeh et al., 2009; Hecht et al., 2005; Hisano et al., 2007; Nelson and shoemaker., 2006; Schmutz et al., 2010; Shoemaker et al., 2004; Wong et al., 2009; Zhu et al., 2005). This information can provide the candidate gene sequences controlling important traits of soybean, and the functional gene analysis tool for soybean is very important to identify the genes associated with important traits, soybean management, and efficient genetic improvements of soybean. Agrobacterium- and biolistic-mediated technologies have been developed for the introduction of foreign genes into plants (Klein et al., 1987; Zambryski et al., 1983). As another approach, a virus vector-mediated gene delivery system has been developed for several plants. Plant virus vectors could be used for both the expression of the foreign genes and the suppression of the target genes by virus-induced gene silencing (VIGS) in infected plants (Gleba et al., 2004; Purkayastha and Dasgupta., 2009). A virus-mediated gene delivery system is quick and does not require transformation and regeneration techniques, and is widely used for functional gene analysis in plants. Especially, it is an attractive tool for major crop plants including soybean, in which efficient transformation is not established for various cultivars. In soybean, five virus vectors were constructed from Clover yellow vein virus (CIYVV), Soybean mosaic virus (SMV), Bean pod mottle virus (BPMV), Cucumber mosaic virus (CMV), and Apple latent spherical virus (ALSV) (Igarashi et al., 2009; Masuta et al., 2000; Nagamatsu et al., 2007; Wang et al., 2006; Zhang and Ghabiral., 2006). CIYVV and SMV have been developed for foreign gene expression by a fusion polyprotein expression strategy, and BPMV- and ALSV-based vectors have been developed for both the expression of foreign genes and VIGS

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of endogenous genes. CMV-based vector has also been used for the induction of VIGS of endogenous genes in soybean. In this chapter, we describe the use of ALSV vector for VIGS of endogenous genes at all growth stages of soybean plants and seeds. We also report the promotion of flowering in soybean, irrespective of their stem-termination type, maturity group, and production area.

2. Apple latent spherical virus (ALSV) and ALSV-based vector ALSV, classified into a genus Cheravirus (Le Gall et al. 2007), has isometric virus particles ca. 25 nm in diameter, and contains two ssRNA species (RNA1 and RNA2) and three capsid proteins (Vp25, Vp20, and Vp24) (Koganezawa et al. 1985; Le Gall et al. 2007; Li et al. 2000) (Fig.1a and b). The virus was originally isolated from an apple tree in Japan (Koganezawa et al. 1985), and has been shown to infect apple latently (Ito and Yoshida 1997). ALSV-RNA 1 is 6813 nt in length excluding the 3’ poly(A) tail and has a single open reading frame (ORF) encoding a replication-associated protein of 243 K (Li et al. 2000). RNA 2 is 3385 nt in length excluding the 3’ poly (A) tail, and also has a single ORF encoding a 42 K movement protein (MP) on the N-terminal side and three capsid proteins in the C-terminal region (Li et al. 2000; Yoshikawa et al. 2006). The genes of both RNA1 and RNA2 are expressed as single polyprotein precursors, and matured viral gene products are yielded by proteolysis of the polyproteins. The MP and three capsid proteins are all indispensable for the cell-to-cell movement of ALSV (Yoshikawa e al., 2006). Vp20, one of the three capsid proteins of ALSV, is a silencing suppressor which interferes with systemic silencing (Yaegashi et al. 2007). Horizontal transmission of ALSV does not occur easily in the field because the natural spread of ALSV from infected trees to neighboring trees has not been observed in the apple orchard since the virus was first detected in 1984 (Nakamura et al., 2010). On the other hand, vertical transmission of ALSV could occur via seed in apple, although the transmission rate is low (Nakamura et al., 2010). ALSV-based vector was constructed from the infectious cDNA clone of RNA2 (pEALSR2) by duplication of protease cleavage site between the C terminus of MP and the N terminus of Vp25 (Li et al., 2004) (Fig.1c). The resulting vector (pEALSR2L5R5) could be used for the expression of foreign genes and for a reliable and effective VIGS among a broad range of plants by co-inoculation with infectious cDNA clone of RNA1 (pEALSR1) (Fig.1c) (Igarashi et al., 2009; Li et al., 2004; Yaegashi et al., 2007; Yamagishi & Yoshikawa, 2009).

3. Efficient inoculation of ALSV vectors to soybean Efficient inoculation of virus vector to plant is one of the important steps for successful use of virus vector. In CIYVV-, SMV-, and BPMV-based vectors, rub-inoculation of DNA clones has been successfully conducted in soybean (Masuta et al., 2000; Seo et al., 2009; Zhang et al., 2010). Unfortunately, direct inoculations of an infectious cDNA clones of ALSV by mechanical or particle bombardment were less efficient in soybean plants. However, infection efficiency of ALSV-based vector has been improved using the biolistic inoculation of total RNAs from ALSV-infected Chenopodium quinoa leaves. By this method, almost 100% of the infection can be achieved consistently in soybean plants (Yamagishi & Yoshikawa, 2009). One of the requirements for plant virus vectors is that the virus does not induce symptoms on plants. In BPMV-based vector, mild strains have been used for vector construction (Zhang et al., 2010). When wild-type (wt)ALSV was inoculated into two cotyledons at the

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Fig. 1. Electron microscopic image (a), diagrammatic representation of ALSV particles (b), and schematic representation of the infectious cDNA clones of RNA1 (pEALSR1) and RNA2–based vector (pEALSR2L5R5) which were constructed by creating artificial protease processing site by duplicating the Q/G protease cleavage site between MP and Vp25 (c). P35S, enhanced CaMV 35S promoter; Tnos, nopaline synthase terminater; PRO-co, protease cofactor; HEL, NTP-binding helicase; C-PRO, cysteine protease, POL, RNA polymerase; MP, 42K movement protein; Vp25, Vp20, and Vp24, capsid proteins.

Fig. 2. Modulating symptoms of ALSV-infected soybean during development (cv. Jack). (a) Right, first and second trifoliolate leaves with mosaic symptom in soybean infected with ALSV at three nodes stage. Left, a non-infected soybean. (b) Right, symptomless third and forth trifoliolate leaves at the later growth stage of infection shown in (a). Left is a noninfected soybean seedling shown in (a).

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emergence stage of soybean, mosaic symptom appeared on unifoliate, first, and second trifoliate leaves (Fig. 2a). However, mosaic symptom is no longer observed above the third trifoliate leaves (Fig. 2b), though the virus is systemically infected in all upper leaves (Yamagishi & Yoshikawa, 2009). We also confirmed that ALSV could infect systemically all eight soybean cultivars (‘Chamame’, ‘Enrei’, ‘Suzukari’, ‘Hatayutaka’, ‘Nemasirazu’, ‘Tanbaguro’, ‘Dewamusume’, and ‘Jack’) with different stem-termination type, maturity group, and production area. The infection of ALSV vectors does not affect the vegetative and reproductive growth of these soybean cultivars even cvs. ‘Tanbaguro’ and ‘Jack’ which are susceptible to all Japanese SMV strains, and cv. ‘Dewamusume’ which is resistant to all Japanese SMV strains and CMV.

4. VIGS in a vegetative stage of soybean plants Phytoene desaturase (PDS) is an enzyme required for biosynthesis of carotenoid which protects chlorophyll from photo-bleaching in plants. When cotyledons of soybean at the emergence stage were inoculated with soyPDS-ALSV containing a 300 bp fragment of the soybean PDS (soyPDS) (Igarashi et al., 2009; Yamagishi & Yoshikawa, 2009), systemic infection was established 1–2 weeks post inoculation (wpi), and all soybean plants infected with soyPDS-ALSV showed an uniform photo-bleached phenotype typical of PDS inhibition on upper, uninoculated leaves, petioles and stems from about 3 wpi (Fig. 3a and Fig.3c

Fig. 3. Phenotype and the accumulation pattern of ALSV RNAs in the leaves of soybean plants infected with soyPDS-ALSV. (a) Right, photo-bleaching of a soybean plant (cv.’Jack’) infected with soyPDS-ALSV 3 weeks post inoculation. Left, non-infected plant. (b) Northern blot hybridization analysis of soybean plants infected with soyPDS-ALSV. Detection of ALSV RNAs (upper panel), soyPDS-mRNA (middle panel), and soyPDS-siRNAs (lower panel) in a non-infected, wtALSV infected, and soyPDS-ALSV infected soybean plants. (c) Detection of the ALSV RNA distribution pattern in the soybean leaves (cv. ‘Tanbaguro’).

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Left, partly photobleached unifoliolate leaf; right, uniformly photobleached third trifoliolate leaf. ALSV signal is strongly detected from the region showing photobleaching. (b) is reprinted with kind permission from Springer Science+Business Media: . upper panel). Northern blot analysis showed that soyPDS mRNA clearly decreased in the photo-bleached leaves infected with soyPDS-ALSV, in contrast with wtALSV-infected leaves which was comparable to non-infected leaves (Fig. 3b middle panel). SoyPDS specific siRNAs, a hallmark of RNA silencing (Hamilton and Baulcombe 1999), also accumulated in the photo-bleached leaves infected with soyPDS-ALSV (Fig. 3b lower panel), indicating that the photo-bleaching of soybean leaves infected with soyPDS-ALSV was due to VIGS of soyPDS. Tissue-blot hybridization showed that soyPDS-ALSV was detected from the photobleached area in infected leaves, indicating that RNA silencing of soyPDS mRNA was induced in the tissues infected with soyPDS-ALSV (Fig. 3c). One of the advantages of ALSV vector for VIGS in plants is that virus was distributed uniformly throughout infected plants and induced a highly uniform RNA silencing phenotype as shown in Fig. 3a. This is due to the fact that ALSV can replicate in the shoot apical meristem and leaf primordia (Fig. 4) and induce VIGS as soon as target gene expresses occurs in newly developed vegetative tissues. In contrast, SMV could not invade into the shoot apical meristem and leaf primordia (Fig. 4).

Fig. 4. In situ hybridization analysis of the distribution of ALSV-RNAs and SMV-RNAs in shoot apices of soybean seedlings (cv. ‘Tanbaguro’). Left, infected; Right, non-infected. ALSV-RNAs could be detected from all tissues including the meristematic tissue and leaf primordia (arrowheads). In contrast, SMV-RNAs were detected in main stem tissues downward of stem apex (arrowheads), but not in the meristematic tissue and leaf primordia. Purple staining indicates the presence of viral RNAs. Scale bars: 200 μm. In situ hybridization analysis of the distribution of ALSV-RNAs is reprinted with kind permission from Springer Science+Business Media: .

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5. VIGS in a reproductive stage of soybean plants infected with soyPDSALSV and soyIFS2-ALSV The VIGS by ALSV vector could be maintained in reproductive growth of soybean, and ALSV could infect about 20-30% of the embryos of seeds on ALSV-infected soybean plants (Yamagishi & Yoshikawa, 2009). Fig. 5a showed the full pod stage of soyPDS-ALSV infected soybean cv. ‘Enrei’, on which photo-bleached full pods were set. All pods and seed coats on soybean plants infected with soyPDS-ALSV showed a highly uniform photo-bleached phenotype similar to leaves of infected plants (Fig. 5b). On the other hand, some embryos showed photo-bleached phenotype and others normal green phenotype (Fig. 5b). Northern blot analysis of infected plants indicated that soyPDS-ALSV was detected in all pods, seed coats, and embryos showing white color phenotype, but not in green embryos (Fig. 5c). SoyPDS siRNAs were also detected in white embryos, but not in green embryos (Fig. 5d), confirming that soyPDS-ALSV could induce VIGS of soyPDS gene in the embryos of seeds on infected plants.

Fig. 5. VIGS in soybean plants at reproductive stage by soyPDS-ALSV. (a) VIGS of soyPDS gene in a soybean plant (cv. Enrei) infected with soyPDS-ALSV at the full pod stage. (b) The seeds in a soybean plant infected with soyPDS-ALSV. Left, colors of both pod and seed coats are white; Right, Longitudinal sections of a pod and seeds. An embryo of one seed shows white color and that of another seed shows green color. (c) Detection of ALSV-RNAs from pods (P), seed coats (SC), and embryos (E) from a soyPDS-ALSV infected soybean plants (cv. Enrei) shown in (b) as seed 1 and seed 2. W and G indicate white and green color, respectively. (d) Detection of soyPDS -siRNAs in the green embryo (GE) and white embryo (WE) shown in (b) as seed 1 and seed 2. Reprinted with kind permission from Springer Science+Business Media: .

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Isoflavone in soybean seeds is one of the functional components which plays diverse roles in plant-microbe interactions (Dixon and Sumner 2003; Subramanian et al. 2005), and it functions in many ways for human health (Chiechi and Micheli. 2005; Cornwell et al. 2004). Isoflavone synthase (IFS) encoded by two genes, IFS-1 and IFS-2, which share 93% nucleotide identities of the coding region, is the key enzyme in the formation of isoflavones in soybean (Subramanian et al. 2005). Inoculation of soyIFS2-ALSV containing 237 bp fragment of the soybean IFS-2 to soybean resulted in the embryo of about 30% of mature seeds being harvested from soybean plants (cv. Enrei) which were infected with soyIFS2ALSV (Yamagishi & Yoshikawa, 2009). RT-PCR analysis showed that the transcripts of soyIFS-2 and soyIFS-1 decreased in the cotyledons of seeds infected with soyIFS2-ALSV. SoyIFS-2-siRNAs were detected in the cotyledons of seeds infected with soyIFS2-ALSV.The isoflavone contents in the cotyledons of mature seeds infected with soyIFS2-ALSV were lower than those in non-infected cotyledons. VIGS of the soyIFS-1 was less efficient than that of the soyIFS-2 gene by soyIFS2-ALSV infection (Yamagishi & Yoshikawa, 2009). Although soyIFS-1 and soyIFS-2 share 92% sequence identity over the 237 nucleotide stretch in soyIFS2-ALSV, the insert has only three identical sequence stretches of more than 23 nucleotides, which is the minimum required for VIGS (Lacomme et al. 2003; Thomas et al. 2001). The sequence identity and the identical sequences stretch may be important for clear VIGS of endogenous gene in soybean by ALSV vectors (Yamagishi & Yoshikawa, 2009).

6. VIGS in an early-growth stage of soybean plants infected with soyPDS-ALSV through seed transmission Growth in the early developmental stages could affect the later growth and final yield in soybean. The VIGS system in early growth stages in soybean will be a valuable tool for functional gene analysis, management, and efficient breeding of soybean. However, it is not easy to establish the systemic infection of virus in very young seedlings like this case just after germination. When the mature seeds from soybean cv.’Enrei’ infected with soyPDSALSV were sown on the soil, about 30% of seedlings showed a highly uniform photobleached phenotype at the emergence stage (Yamagishi & Yoshikawa, 2009). The photobleaching was maintained in their cotyledons, hypocotyls, and unifoliate leaves after emergence as shown in Fig. 6a. Both soyPDS-ALSV and soyPDS-siRNAs were detected from the photo-bleached seedlings, but not from the green seedlings (Fig. 6b and c). The other seedlings (about 70%) from infected soybean cv.’Enrei’ grew as normal green plants and were not infected with soyPDS-ALSV.

7. The promotion of flowering in soybean plants by ALSV vector Soybean, which originated in China, is a short days plant and has been adapted to various climates and environments during cultivation. It has resulted in many variations in flowering traits, for example, different maturity groups by differences of photoperiod sensitivity (Fukui and Arai, 1951; Fukui 1963; Shanmugasundaram, 1981), two types of stem growth habit, one is determinate which loses vegetative activity on the stem apex when reproductive growth starts, the other is indeterminate which can maintain vegetative activity on the stem apex through reproductive growth (Bernard, 1972; Tian et al., 2010), and the long juvenile period characteristic under short-day condition (Neumaier and James,1993;

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Carpentieri-Pípolo et al., 2002). The variation of requirements in flowering might have been an obstacle to efficient soybean breeding. For example, soybean varieties adapted to low latitudes are difficult to induce flowering in a high latitude climate despite the importance of genetic resources. The flowering integrator gene FLOWERING LOCUS T (FT) in Arabidopsis thaliana is conserved between diverse plant species, and FT protein is thought to be the flowering signal ‘‘florigen’’, a universal long-distance mobile signal. FT protein interacts with the bZIP transcription factor, FD, and activates floral identity genes in the meristematic tissue of the shoot apex (Abe et al., 2005; Corbesier et al., 2007; Jaeger and Wigge, 2007; Mathieu et al., 2007; Wigge et al., 2005). In soybean, Kong et al. Identified ten FT homologs. Ectopic expression in A. thaliana confirmed that two FT homologs, GmFT2a and GmFT5a, had the same function as A. thaliana FT (Kong et al., 2010). On the other hand, the expression of FT gene from A. thaliana promoted precocious flowering in heterologous plant species, such as cucurbits and Solanaceae (Lin et al., 2007; Li et al., 2009; Yamagishi et al., 2011).

Fig. 6. VIGS of soyPDS gene in the next generation plant (cv. Enrei) from a plant infected with soyPDS-ALSV. (a) The next generation soybean seedlings. Left, a non-transmitted green seedling; Right, a soyPDS-silenced white seedling by seed transmission of soyPDSALSV. Both seedlings were germinated from seeds produced on a same soybean plant showing photobleaching. (b) and (c) Detection of ALSV-RNAs (b) and soyPDS-siRNAs (c) from cotyledon(C), leaf (L), and root (R) from a non-transmitted (green) and a soyPDSALSV seed transmitted (white) seedlings shown in (a). G and W indicate green and white color. (b) and (c) are reprinted with kind permission from Springer Science+Business Media: . When FT-ALSV containing full-length sequence of FT was inoculated into the cotyledons of soybean 2-3 days after germination, soybean seedlings showed precocious flowering irrespective of the maturity groups in soybean (Yamagishi & Yoshikawa, 2011). For example, in Saitama Prefecture in Japan, it takes about 70 days and 90 days to blooming from germination in cvs. ’Suzukari’ and ’Tanbaguro’, respectively, (Fukui 1962). Both cultivars produced the flower buds on the main stem apex at 7 node stages at about 40dpi when FT-

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ALSV was inoculated to the cotyledons 2-3 days after germination. Furthermore, indeterminate cultivars ‘Dewamusume’ and ‘Jack’ infected with FT-ALSV also produced the flower bud on the main stem apex under the long-day condition at about 40 dpi similar to that of determinate cultivars (Fig.7a) (Yamagishi & Yoshikawa, 2011). In the same conditions, noninfected soybean cvs. ’Dewamusume’ and ‘Jack’ continued vegetative growth until at least 3 month-post inoculation (mpi) (Yamagishi & Yoshikawa, 2011). Taken together with these results, FT-ALSV infection could terminate the vegetative growth of soybean by production of flower buds on the main stem apex irrespective of their flowering traits. Fig. 7b shows the phenotypes of FT-ALSV- infected and non-infected cv.’Dewamusume’ at about 4 mpi. The pods and seeds on infected plant had matured after flowering (Fig. 7b left), in contrast with non-infected soybean which continues the vegetative growth under the same condition (Fig. 7b right). Seeds from these precocious flowering soybeans were normal

Fig. 7. Precocious flowering and termination of the vegetative growth of soybean by FTALSV infection. Plants were grown under a long-day condition. (a) The flower buds formation on the main stem apex of indeterminate cultivar ‘Dewamusume’ infected with FT-ALSV 38 dpi. (b) Comparison of an FT-ALSV- infected soybean cv. ‘Dewamusume’ shown in (a) about 120 dpi (left) and a non-infected control soybean cv. ‘Dewamusume’ (right). After the production of flower buds on the main stem apex, vegetative growth was terminated in FT-ALSV-infected soybean. Seeds matured on the soybean infected with FTALSV, in contrast with a non-infected plant which continues vegetative growth under the same condition.

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in appearance and began germination when water was absorbed (Yamagishi & Yoshikawa, 2011). Thus, it is possible to reduce the generation time of soybean plants by FT-ALSV infection (Yamagishi & Yoshikawa, 2011). Although FT-ALSV transmitted through seeds to the next generation plants, virus-free plants also could be easily obtained. We think that precocious flowering by FT-ALSV in soybean would be a novel technology to obtain the gametophytoes for crossing and reducing the generation time in soybean breeding.

8. Consideration and prospects As described above, ALSV vectors have several advantages for use as VIGS vectors in soybean. First, ALSV does not induce leaf symptoms above the third trifoliate leaves, and it does not affect the vegetative and reproductive growth of soybean plants. Second, ALSV can infect systemically various soybean cultivars with different stem-termination type, maturity group, and production area. Third, ALSV can infect systemically soybean plant including meristematic tissue and induce a highly uniform RNA silencing phenotype in upper leaves. Fourth, ALSV vector can induce VIGS of endogenous genes in all soybean growth stages including reproductive stage, seeds, and early growth stages of next generation. On the other hand, ALSV vector cannot be applied to high-throughput analysis because of its gene expression strategy of viral genome (Igarashi et al., 2009). Recently, Zhang et al. have developed new BPMV-based vector, in which the cloning site for foreign sequences was introduced just after stop codon of a polyprotein in RNA2 (Zhang et al., 2010). This makes it possible to use BPMV-based vector for high-throughput analysis. This improvement can be applied to ALSV vector as well. Infection of FT-ALSV induced precocious flowering in soybean, irrespective of the different stem growth habits and maturity types of the cultivars. This rapid flowering system using FT-ALSV will provide a powerful method for soybean breeding, in combination with molecular markers and genome sequence data.

9. Acknowledgements This work was supported in part by Grants-in-Aid from KAKENHI (no. 20380025).

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flavonoid biosynthesis by virus-induced gene silencing. Plant Biotechnology Journal, Vol. 5 (No.6): 778–790. Nakamura, K., Yamagishi, N., Isogai, M., Komori, S., Ito, T. & Yoshikawa, N. (2010). Seed and pollen transmission of Apple latent spherical virus in apple. Journal of General Plant Pathology (in press). Nelson, RT. & Shoemaker, R. (2006). Identification and analysis of gene families from the duplicated genome of soybean using EST sequences. BMC Genomics, Vol. 7: 204 Neumaier, N. & James, AT. (1993). Exploiting the long juvenile trait to improve adaptation of soybeans to the tropics. Food Legume Newsletter 18:12-14. Purkayastha, A & Dasgupta, I. (2009). Virus-induced gene silencing: A versatile tool for discovery of gene function in plants. Plant Physiology and Biochemistry Vol.47 (No.11): 967-976. Schmutz, J., Cannon, SB., Schlueter, J., Ma, J., Mitros, T., Nelson, W., Hyten, DL., Song, Q., Thelen, JJ., Cheng, J., Xu, D., Hellsten, U., May, GD., Yu, Y., Sakurai, T., Umezawa, T., Bhattacharyya, MK., Sandhu, D., Valliyodan, B., Lindquist, E., Peto, M., Grant, D., Shu, S., Goodstein, D., Barry, K., Futrell-Griggs, M., Abernathy, B., Du, J., Tian, Z., Zhu, L., Gill, N., Joshi, T., Libault, M., Sethuraman, A., Zhang, XC., Shinozaki, K., Nguyen, HT., Wing, RA., Cregan, P., Specht, J., Grimwood, J., Rokhsar, D., Stacey, G., Shoemaker, RC. & Jackson,SA. (2010). Genome sequence of the palaeopolyploid soybean. Nature Vol.463 (No.14): 178-183. Seo, J-K., Lee, H-G. & Kim K-H. (2009). Systemic gene delivery into soybean by simple rubinoculation with plasmid DNA of a Soybean mosaic virus-based vector. Archives of Virology Vol.154 (No.1): 87-99. Shanmugasundaram, S. (1981). Varietal differences and genetic behavior for the photoperiodic responses in soybeans. Bulletin of Institute of Tropical Agriculture, Kyushu University 4:1-61. Shoemaker, RC., Cregan, PB. & Vodkin, LO. (2004). Soybean genomics, in Boerma, HR., Specht (eds.), SOYBEANS: Improvement, Production, and Uses, 3rd Edition. American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, Madison, Wisconsin, USA, pp. 235-264. Subramanian, S., Graham, MY., Yu, O. & Graham, TL. (2005). RNA interference of soybean isoflavone synthase genes leads to silencing in tissues distal to the transformation site and to enhanced susceptibility to Phytophthora sojae. Plant Physiology Vol. 137 (No. 4):1345–1353. Thomas, CL., Jones, LDC. & Maule, AJ. (2001). Size constraints for targeting posttranscriptional gene silencing and for using RNA directed methylation in N. benthamiana using a potato virus X vector. Plant Journal Vol. 25., No.4., February 2001., 417–425. Van Kammen, A. (1997). Virus-induced gene silencing in infected and transgenic plants. Trends in Plant Science Vol. 2 (No. 11): 409-411. Wang, L., Eggenberger, A., Hill, JH. & Bogdanove, AJ. (2006). Pseudomonas syringae effector avrB confers soybean cultivar-specific avirulence on Soybean mosaic virus adapted for transgene expression but effector avrPto does not. Molecular Plant-Microbe Interaction Vol. 19 ( No.3): 304–312.

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Wigge, PA., Kim, MC., Jaeger, KE., Busch, W., Schmid, M., Lohmann, JU. & Weigel, D. (2005). Integration of spatial and temporal information during floral induction in Arabidopsis. Science Vol. 309 (No.5737): 1056-1059. Wong, CE., Singh, MB. & Bhalla, PL. (2009). Molecular processes underlying the floral transition in soybean shoot apical meristem. The Plant Journal Vol. 57 (No. 5): 832845. Yaegashi, H., Yamatsuta, T., Takahashi, T., Li,C., Isogai, M., Kobori, T., Ohki, S. & Yoshikawa, N. (2007). Characterization of virus-induced gene silencing in tobacco plants infected with Apple latent spherical virus. Archives of Virology Vol. 152 (No.10):1839–1849. Yamagishi, N. & Yoshikawa, N. (2009). Virus-induced gene silencing in soybean seeds and the emergence stage of soybean plants with Apple latent spherical virus vectors. Plant Molecular Biology Vol. 71(No.1): 15-24. Yamagishi, N., Sasaki, S., Yamagata, K., Komori, S., Nagase, M., Wada, M., Yamamoto, T. & Yoshikawa, N. (2011). Promotion of flowering and reduction of the generation time in apple seedlings by ectopical expression of the Arabidopsis thaliana FT gene using the Apple latent spherical virus vector. Plant Molecular Biology Vol. 75 (No. 12): 193-204. Yamagishi, N. & Yoshikawa, N. (2011). Expression of FLOWERING LOCUS T from Arabidopsis thaliana induces precocious flowering in soybean irrespective of maturity group and stem growth habit. Planta (in press) doi: 10.1007/s00425-0101318-3 Yoshikawa, N., Okada, K., Asanuma, K., Watanabe, K., Igarashi, A., Li, C.& Isogai, M. (2006). A movement protein and three capsid proteins are all necessary for the cellto-cell movement of apple latent spherical cheravirus. Archives of Virology Vol. 151 (No. 5): 837-848. Zambryski, P., Joos, H., Genetello, C., Leemans, J., van Montagu, M. & Schell, J. (1983). Ti plasmid vector for introduction of DNA into plant cells without alteration of their normal regeneration capacity. The EMBO Journal Vol. 2 (No. 12): 2143-2150. Zhang, C. & Ghabrial, SA. (2006). Development of Bean pod mottle virus based vectors for stable protein expression and sequence-specific virus-induced gene silencing in soybean.Virology Vol. 344 (No. 2): 401-411. Zhang, C., Yang, C., Whitham, SA. & Hill GH. (2009). Development and use of an efficient DNA-based viral gene silencing vector for soybean. Molecular Plant-Microbe Interaction Vol. 22 (No. 2): 123-131. Zhang, C., Bradshaw, JD., Whitham, SA. & Hill GH. (2010). The development of an efficient multipurpose Bean pod mottle viral vector set for foreign gene expression and RNA silencing. Plant Physiology Vol.153 (No. 1): 52-65. Zhu, H., Choi, H-K., Cook, DR. & Shoemaker, RC. (2005). Bridging model and crop legumes through comparative genomics. Plant Physiology Vol. 137 (No.4): 1189-1196.

4 Genetic Improvement: Molecular-Based Strategies Aleksandra Sudarić1, Marija Vratarić1, Snežana Mladenović Drinić2, and Zvonimir Zdunić1 2Maize

1Agricultural Institute Osijek, Osijek Research Institute, Zemun Polje, Belgrade 1Croatia 2Serbia

1. Introduction Soybean (Glycine max (L.) Merr.) is the world’s primary source of protein feed supplement for livestock and accounts for much of the world’s vegetable oil supply. Additionally, healthy aspects of soyfoods go beyond the oil and protein and include minor compounds with nutraceutical properties such as isoflavones, saponins and tocopherols (Rajcan et al., 2005). Over the past three decades, world production of soybean has tripled, from 75 449 966 t in 1978 to 230 952 636 t in 2008 (www.fao.org), what is attributed to the scientific and technological developments in most regions as well as increasing world population, consumer acceptance and consumption of soybean in non-traditional regions of the world. All the sectors, involved with the entire soybean production and processing chain, have responded accordingly to comply with the demands of a globalize economy. The genetic improvement of soybean, based on breeding strategies, contributes to advances in production and food processing industry by developing high-yielding and high-quality soybean cultivars, hereby enhancing value-added, healthy and safe properties of final soy products. Yield has been and remains the trait of greatest emphasis by breeders, as it is the trait with the greatest effect on a producer’s net income. Studies of genetic progress reported that yields increased about 15 to 38 kg ha-1 annually over the period of seventy years (Specht et al., 1999; Wilcox, 2001; Ustun et al., 2001; Egli, 2008). Besides yield, progress has also been made in selecting for resistance to pathogens, insects and nematodes, tolerance to other production hazards, improvement in seed protein and oil, as well as other agronomic characteristics. The genetic improvements have been accomplished mainly through the use of conventional (also termed empirical or traditional) breeding. The conventional breeding strategies are based on crossing, selection and fixation of superior phenotypes to develop improved cultivars and breeds suited to specific conditions with the aim to fulfill the needs of farmers and consumers. As the result of soybean self-pollinating reproductive behavior, conventional breeding procedures such as pedigree breeding, single pod descent, backcrossing and bulk population breeding are some of the more common procedures used to develop soybean cultivars. Although progress in soybean breeding accomplished only by

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conventional breeding methods is significant, for further genetic advances in soybean germplasm the use of conventional breeding methods exclusively is no longer sufficient. There are multiple reasons for that. First of all, the development of new cultivar with conventional breeding methods requires at least ten generations. The length of this process is often in disproportion with rapid changes in market demands. In fact, on global level, changes in climate, soil structure and fertility, production technology, appearance of new phytopathogen races etc. became so rapid that the cultivar developed by conventional hybridization of parents with desired traits about ten years ago is no longer capable of accomplishing its genetic potential due to environmental stress factors. In addition, the burden of undesired genetic material (material incompatible with set breeding aims) constitutes a big problem in conventional breeding, because the elimination of undesired phenotypes requires more area, more time and thus bigger investments. In classical genetic improvement programs, selection is carried out based on observable phenotypes of the candidates for selection and/or their relatives but without knowing which genes are actually being selected. Plant scientists have made significant advances in understanding the agronomical, species-specific, breeding, biochemical and molecular processes that underlie important genetic, physiological and developmental traits, or that affect the ability of plants to cope with unfavorable environmental conditions for several decades (Gepts, 2002). The discoveries and implementations of biotechnology and molecular biology for selection purposes provide a stable background for generating of new knowledge and practical use in agricultural research and practice as well as to meet the growing demand for more and with better quality food and feed (Todorovska et al., 2010). Main objectives of plant biotechnology are attempts to engineer metabolic pathways for the production of tailor-made plant polymer or low molecular weight compounds and the production of novel polypeptides for pharmaceutical or technical use. In general, goals of plant biotechnology are not much different from classical breeding goals. They can be divided into attempts to optimize input and output traits. Input traits refer to increased resistance towards abiotic and biotic stress, strategies to increase crop yield and to improve post-harvest characteristics. Attempts to improve output traits include production of foreign proteins for pharmaceutical and technical use, production of endogenous or novel polymers for food and non-food applications as well as synthesis of low molecular weight compounds including vitamins, essential aminoacids and pharmaceutically relevant secondary plant products (Sonnewald & Herbers, 2001). Scientists in the laboratory can genetically engineer soybean plants with unique genes, but plant breeding is necessary to put the new transgenes via sexual reproduction into the proper genetic background so that it is adapted to the intended areas of use. Recent developments in molecular biology and genomics are greatly accelerating the speed with which knowledge gained in basic plant science can be applied to species improvement (Dekkers & Hospital, 2002). Therefore, the molecular based plant breeding techniques are assuming an increasingly more important role in genetic improvement of soybean germplasm. Currently, conventional breeding strategies have priority, and in combination with molecular technologies have provided the possibility of broadening genetic variability of cultivated soybean as well as development of new germplasm that is better adapted to new market, production and environment demands (Verma & Shoemaker, 1996; Orf et al., 2004; Sudarić et al., 2008, 2010; Vratarić & Sudarić, 2008; Mladenović Drinić et al., 2008; Cober et al., 2009). Modern biotechnology application in soybean breeding can be divided in two major categories:

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molecular genetics and, genetic transformation. Molecular genetics studies how genetic information is encoded within the DNA and how biochemical processes of the cell translate the genetic information into the phenotype. Genetic transformation involves the alteration of the genetic constitution of cells or individuals by directed and selective modification, insertation of native or foreign gene, or deletion of an individual gene or genes. According to Shoemaker et al. (2004), soybean has emerged as a model crop system because of its densely saturated genetic map, a welldeveloped genetic transformation system and the growing number of genetic tools applicable to this biological system. The emphasis in this chapter will be on selected information about new technological developments derived from molecular biology for soybean breeding purposes. Two main aspects will be considered: the use of genetic markers and transformation (genetic modification).

2. Genetic marker systems The term genetic markers refers to genes with known locations in chromosomes, that are clearly visible on phenotypic level, and are easy to track in series of consecutive generations. Furthermore, it is defined as any DNA fragment that displays some form of observable polymorphism in analysed individuals (Hill et al., 1998; Liu, 1998; Konstantinov et al., 2004). Three types of genetic markers have been used in soybean genomic analysis: morphological markers, protein based markers (biochemical markers), DNA based markers (molecular markers). According to Liu (1998), to be a genetic marker, the marker locus has to show experimentally detectable variation among the individuals in the test population. The variation can be considered at different biological levels, from the simple heritable phenotype to detection of variation at the single nucleotide. Once the variation is identified and the genotypes of all individuals in the test population are known, the frequency of recombination events between loci is used to estimate linkage distance between markers. Different types of markers may identify different polymorphisms. The utilisable value of the certain genetic marker system depends on several parameters such as: reliability determined by the analysis results, reproducibility, universality or independence from specific plant material in tests, high level of observed polymorphism, random distribution of marker in the genome and interesting location on genetic map, which implies the existence of gene– marker relationship for the trait of interest. Likewise, the genetic interpretation of markers strongly depends on the sequence complexity of the genome and the kind of variation the marker identifies. 2.1 Morphological markers Morphological traits such as shape, colour, size or height often have one to one correspondence with the genes controlling the traits. In such cases, the morphological characters (the phenotypes) can be used as reliable indicators for specific genes and are useful as genetic markers on chromosomes. Since the number of morphological traits of certain species is limited, so is the number of morphological markers. Morphological markers are usually easy to observe, but it is difficult to have a large number of them

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segregating in a single or few populations. To obtain a reasonable number of polymorphic morphological markers, many mapping populations are needed (Liu, 1998). Furthermore, many morphological traits are visible only on certain plant part and only in certain stage of plant development (Fig. 1) which further limits the time of use. Morphological markers are in selection process used mainly in early generations, for description and/or identification of genotypes in germplasm collections (gene banks) and in the procedure of variety’s official released (lists of descriptors from the International Union for the Protection of New Varieties of Plants (UPOV).

Fig. 1. Flower colour of soybean – morphological marker (photo: Vratarić, M.) 2.2 Protein based markers Proteins are result of gene expression. Different alleles of genes may result in proteins with different aminoacid compositions, size or modifications. Differences in charge and size can be easily detected using gel electrophoresis and can be used as genetic markers. Protein markers are divided in two groups: storage proteins; functional proteins or isozymes. Seed proteins, as specific gene products, could indicate genetic specify of genotypes, and therefore could be used as markers for characterization of varieties, to resolve taxonomic relationships or for seed purity testing (Bushehri et al., 2000; Nikolic et al., 2004, 2005; Konstantinov et al., 2005; Malik et al., 2009). The SDS-PAGE is a practical and reliable method for species identification because seed storage proteins are largely independent of environmental fluctuation (Gepts, 1989). Genetic diversity and the pattern of variation in the soybean genotypes have been evaluated with seed protein (Alipour et al., 2002; Bushehri et al., 2000, Nikolic et al., 2005, Malik et al., 2009). In soybean, plant with a narrow genetic base in their pool, protein markers are not sufficient for characterization and study genetic diversity (Nikolic et al., 2005). Srebric et al (2010) identified Kunitz –free progeny of cross between common varieties Kador and Kunitz by electrophoresis of seed proteins (Fig. 2). The isozymes are commonly used type of protein marker, which have been used for several decades. Isozymes are proteins with small differences in amino acid content, which catalyze the same chemical reaction, but are under the control of different genes. Isozymes as markers are co-dominant, they don't undergo epistatic interactions with other molecular markers, and their expression does not stand under the influence of environment. They are limited in number and tissue and are developmental-stage dependent. Before DNA markers were discovered, isozymes were extensively used in many plant species and are still often used in conjunction with DNA markers.

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KTI

Fig. 2. SDS PAGE patterns of protein from soybean genotypes. KTI-Kunitz Trypsin Inhibitor. 1,9,10 genotypes without KTI; 2-8 genotypes with KTI (photo: Srebric, M.) In soybean, isozyme polymorphism has been used for characterising and identifying genotypes and varieties (Cardy & Beversdorf, 1984; Doong & Kiang, 1987), for studying population genetics (Miroslav & Jiri, 1996), for examining geographical patterns of variation (Griffin & Palmer, 1995; Hirata et al., 1999), in seed production for determining uniformity and genetic purity of cultivars and identifying different varietals impurities in seed material. Bushehri et al. (2000) evaluated twenty one soybean (Glycine max) cultivars electrophoretically for the banding pattern of storage proteins and suggested that SDSPAGE is a more powerful tool to characterize soybean cultivars compared to isozyme patterns. 2.3 DNA based markers (molecular markers) Molecular marker refers to the DNA sequence with exactly defined nucleotide order and distribution, strictly specific for different organisms. According to Liu (1998) two basic approaches have been used to detect variation in the small region of DNA. The fragment can be detected by nucleic acid hybridization, which uses another fragment from the same locus which has been isolated and purified from the same or related species. The previously known segment must share considerable DNA sequence homology with the fragment of interest and can be labelled and used as a probe to detect the fragment of interest by complementary base pairing. The second approach is based on the amplification of sequences using polymerase chain reaction (PCR). To amplify a target segment, two primers designed using known sequences of the segment are needed. In plant breeding, molecular markers have several advantages over the traditional phenotypic markers: accuracy, reliability, speed, indifference to the conditions under which the plants are grown and detectability in all stages of plant growth. Mode of action, level of polymorphism, informativeness, developmental cost, number of sample that could be run, level of skill, reliability are important considerations when selecting markers for specific applications. Although each marker system is associated with some advantages and disadvantages, the choice of the system is dictated by the intended application, equipment and the cost involved. In soybean breeding, molecular marker applications are currently focused in four areas: germplasm characterization, marker-assisted selection (MAS), marker-assisted backcrossing and gene discovery.

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Knowledge of genetic diversity in soybean elite breeding material has a significant impact on the improvement of plants, efficient utilization of eligible germplasm polymorphism and genotype selection for different breeding objectives. Genetic diversity can be assessed based on pedigree analysis, phenotypic data or molecular markers. Pedigree analysis and phenotypic evaluation of genetic diversity have a number of limitations, which have largely been exceeded by the development of molecular markers. Their advantage is based on fact that molecular markers are almost unlimited in number and are not influenced by environmental factors. Therefore, molecular markers have a significant role in estimating the diversity degree and genetic constitution of the existing germplasm, as well as, in the predicting of the heterotic effects based on the genetic distance between the parents in hybrid programmes. Genetic marker information in parental selection could have a favourable impact on breeding efficiency and contribute to the faster achievement of genetic improvement in soybean. The implementation of molecular markers closely associated with desirable traits is being used to increase the efficiency and effectiveness of conventional breeding by indirect selection of the desirable plants in segregating population. Such selection approaches, based on the use of markers and called in general MAS has been used to increase the probability of identifying truly superior genotypes, by focusing on determination of genotypes with superior potential, and by enabling simultaneous improvement for traits that are negatively correlated (Knapp, 1998; Dekkers & Hospital, 2002; Todorovska et al., 2010). MAS is used more readily than the usual techniques to screen single traits, such as resistance or restorer genes: nematode resistance, insects resistance, pathogen resistance. The combination of reliable phenotyping and MAS has been particularly important in transferring desirable alleles by simple backcrossing into elite germplasm. Application of markers of introgression programs can result in a reduction in the number of breeding cycles by improving selection efficiency, particularly at the early stage. Also, molecular markers allow us to identify and map the Quantitative Trait Loci (QTLs), i.e. the relevant loci responsible for genetic variability of quantitatively-inherited traits. Because of the genetic by environment interactions on most quantitative traits, breeding for them requires replicated field trials conducted over 2 or more years in different locations. This is obviously time consuming and expensive. The ability to select for an easily identifiable marker that is a good predictor of the presence or absence of a QTL trait can save time and money in a breeding program. Discovery and tagging of QTL is a prerequisite of this type of MAS (Shoemaker et al., 2004). In soybean, different DNA marker systems such as Restriction Fragment Length Polymorphism (RFLP), Randomly Amplified Polymorphic DNAs (RAPD), DNA Amplification Fingerprinting Markers (DAF), Simple Sequence Repeats (SSR), Amplified Fragment Length Polymorphism (AFLP) and Single Nucleotide Polymorphism (SNP) have been developed and applied. 2.3.1 Restriction Fragment Length Polymorphism Restriction Fragment Length Polymorphism (RFLP) represents the first generation of DNA markers used for plant genomes (Weber and Helentjaris, 1989). The basis of RFLP is using restriction enzymes (endonucleases), which recognise short DNA fragments (3-6 bases) and cut the DNA at sequence-specific sites. Polymorphism of genomic DNA detected through DNA fragment length after its digestion with restriction enzymes is due to variability in

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number and array of restriction sites, which are recognised by restriction endonucleases. The next step is to separate the paired strands of the DNA fragments (denaturation) by putting the gel in alkali. The denatured single-strand DNA fragments are usually transferred to a nylon or nitrocellulose membrane filter such that the fragments retain the same pattern on the membrane as on the gel. This blotting procedure was first applied to DNA analysis by E. Southern and is still referred to as a Southern blot (Southern, 1975). The final step of the Southern blotting is to visualize specific DNA fragments using a radioactively labelled probe. Alternatively, non-radioactive probes can be used. In this case, the hybridized fragments are then detected by non-radioactive assays involving chemiluminescence of colorigenic detection. The result is ideally a series of bands on a gel which can then be scored for presence or absence of particular bands. Differences between genotypes are usually visualised as an altered pattern of DNA restriction fragments. Application of RFLPs has advantages and disadvantages. The main advantages of RFLP methodology are: results are highly reproducible between laboratories, co-dominant markers and simplicity of the method. Disadvantages are: time consuming, expensive to perform and using radioactively labelled probes. In soybean, the use of RFLPs started in the late 1980s (Apuya et al., 1988; Keim et al., 1990) which contributed the development of first genetic map of soybean genome which contained 150 RFLP markers with a total length of about 1200 recombination units (Keim et al., 1990). This map, developed jointly by the USDA-ARS (U.S. Department of AgricultureAmerican Research of Soybean), Iowa State University and ASA (American Soybean Association) and referred to as the ˝Public˝ map saw further expansion during the 1990s with the addition of RFLP loci (over 350) (Shoemaker and Olson, 1993). At the same time, the soybean genome map with over 600 identified loci was developed (Rafalski and Tingey, 1993). These initial maps were constructed using populations created from crosses among cultivated and wild soybean, because large proportion of the loci on these maps would not be expected to segregate in crosses among cultivated soybean genotypes. Despite the great utility of RFLP based molecular genetic linkage maps substantial effort was expended in the development of alternative markers that detected greater levels of molecular genetic variation. In addition, the duplicated nature of the soybean genome, to which RFLP probes will hybridize on an average of 2.55 times (Shoemaker, et al., 1995) created complications with the use of RFLP. Thus, in the early 1990’s, PCR-based marker systems including RAPD, SSR and AFLP markers began to be developed and used by soybean geneticists (Cregan, 1999). 2.3.2 Randomly Amplified Polymorphic DNA The basis of Randomly Amplified Polymorphic DNA (RAPD) markers is the polymerase chain reaction (PCR) amplification with arbitrarily chosen primers that initiate DNA synthesis from sites to which the primer is matched. So, RAPDs were designed to solve the problem of lack of pre-existing DNA sequence information. A single, arbitrarily chosen short oligonucleotide can be used as a primer to amplify genome segments flanked by two complementary primerbinding sites in inverted orientation (Williams et al., 1990). If the sites occur on opposite strands of a segment of DNA in inverted orientation and the distance between the sites is short enough for PCR, then the segment flanked by the sites can be amplified. Polymorphism of genomic DNA is detected through the length of synthesized DNA fragments. If polymorphism exists in the binding sites among different genotypes or the fragment length differs at the same site from genotype to genotype, then a RAPD marker is obtained.

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In practice, only small amounts of DNA from each genotype are needed as templates. PCR reaction can be carried out in a 96-well plate using a programmable thermocycler. The amplification products are separated on an agarose gel and visualized using ethidium bromide staining. The key point about this technique is that nothing is known about the identity of the amplification products. However, the amplification products are extremely useful as markers in genetic diversity studies. Other important features of the technique are: simplicity, unit costs per assay are low, RAPDs are dominant, and reproducible results can be obtained if care is taken to standardise the conditions used (Ford-Lloyd & Painting, 1996). RAPD markers for analysing plant genome were developed by Welsh & McClelland (1990), and by Williams et al. (1990). The use of RAPDs for analyzing soybean genome was started in the early 1990s. The RAPD markers have been widely used for genetic diversity study of soybean germplasm (Correa et al., 1999; Baranek et al., 2002; Nikolic et al., 2007; Peric et al., 2008a) (Fig. 3.).

(a)

(b)

Fig. 3. (a) RAPD polymorphism in 6 soybean genotypes with primer GEN 4-70-9 (arrow marked unique band in genotype 4) photo: Mladenović Drinić, S.) (b) RAPD polymorphism in 13 soybean genotypes (photo: Mladenović Drinić, S.) In studies of genetic diversity, researchers are interested in clustering similar individuals, so that the greater difference occurs among the formed groups. Statistical methods, as cluster analysis, factor analysis and principal component analysis can be applied to help in this kind of study. Among them, cluster analysis stand out as it does not demand an initial hypothesis regarding the probability distribution of the data and as it provides easy interpretation. Considering that the results of clustering can be influenced by the similarity coefficient choice, these coefficients need to be better understood so that the most efficient ones in each specific situation can be employed. For example, Mladenovic Drinic et al. (2008) were investigated the influence of four similarity coefficients (Sorensen-Dice (D), Jaccard (J), Roger & Tanimoto (RT), Simple-matching (SM)) over the following cluster analysis, based on data from RAPD marker analysis of twenty soybean genotypes. PCR amplification of genomic DNA was performed using 27 RAPD primers. Eighteen RAPD primers showed clear and reproducible bands, while 9 primers didn’t showed amplification at all, or bands were smeared and weak. Totally 86 RAPD fragments of different molecular weight were observed out of which 37.2% were polymorphic. Results were indicated on a low genetic diversity of soybean. Genetic similarity ranged from 0.845 to 0.986 (D); 0.607-0.954 (RT), 0721-0.977 (SM) and 0.7317-0.9718 (J). When dendograms were contrasted by the CIc index, small differences among them were made evident (Fig. 4).

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(a)

(b) Fig. 4. (a) Dendogram of 20 soybean genotypes based on RAPD markers using Sorens-Dice coefficients (b) Dendogram of 20 soybean genotypes based on RAPD markers using Roger & Tanimoto coefficients The dendogram obtained by Rogers & Tanimoto coefficient was identical to that of Simple matching as were Sorensen-Dice and Jaccard. Consequently, results of RAPD markers

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analysis can provide the previous information on the genetic similarity of parents, and based on it, the performance of traits in the progeny can be predicted, as well as proportion of superior progenies generated by each cross in advanced generations of selfing (Barroso et al., 2003; Peric et al. 2006, 2008b). 2.3.3 DNA Amplification Fingerprinting Markers Similar to RAPD markers, the basis of DNA Amplification Fingerprinting Markers (DAFs) is the use of PCR with arbitrarily chosen primers, i. e. DAF markers are amplified with the use of a single arbitrarily chosen primer. The procedure was described by Caetano-Anolles et al. (1992). The basic differences between RAPD and DAF technologies are: DAF has shorter arbitrarily chosen primers (usually 5-8 nucleotides), so for the electrophoresis of DAFs we use polyacrylamid gel with silver staining, and for RAPDs we use agarose gel. The use of DAF markers in soybean genome analyses started during 1990s. A limited number of DAF-generated polymorphisms were mapped in the University of Utah, Minsoy x Noir 1 RIL population (Prabhu & Gresshoff, 1994). The authors noted that DAF-generated polymorphic markers occur frequently and reliably, that they are inherited as Mendelian dominant loci and that they can be used in genome mapping. 2.3.4 Simple Sequence Repeats (microsatellites) The use of simple (short) sequence repeats (SSRs) (small DNA fragments, usually 2-5 bp long) is based on amplification of short DNA fragments with repeating core motif (repeats 9-30 times). Polymorphism of genomic DNA is detected through the number of short repeat units after amplification in polymerase chain reaction with the use of primers which limit the loci of satellite DNA. Microsatellites have high level of variability in many plant and animal species. Most common forms of repeat units are simple di-nucleotides like (CA)n:(GT) n, (GA) n:(CT) n, (CG) n:(CG):(GC) n, and (AT) n:(TA) n (n is number of repeats), while microsatellites with 3 or 4 nucleotides are rare. The most common motifs in soybean are: AT, ATT, TA, TAT, CT, CTT (Mohan et al., 1997). First applications of SSRs in plant genome analyses were in soybean. In early 1990s, two scientific groups (Akkaya et al., 1992; Morgante & Olivieri, 1993) published similar results demonstrating high levels of polymorphism, co-dominance and locus specificity for SSR markers in soybean. Because of the numerous advantages (high level of polymorphism, single locus nature, random distribution in the genome), SSR markers are excellent complement to RFLP markers for soybean researches in the fields of molecular biology, genetics and plant breeding. The development and mapping of a large set of soybean SSR markers was initiated in 1995 with the support of the United Soybean Board, resulting in development of more than 600 SSR loci. These loci were mapped in three different mapping populations (Cregan et al. 1999). One of these was the USDA/Iowa State population. Second was the 240 RIL University of Utah populations developed from a cross of the cultivated soybean genotypes Minsoy and Noir 1. The third was the University of Nebraska Clark x Harosoy isoline population consisting of 57 F2-derived lines. These three separate maps provided useful information relative to the consistency of marker order and genetic distance among the different populations. Song et al. (2004) reported that the 420 newly developed SSRs were

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mapped in one or more of five soybean mapping populations: Minsoy x Noir 1, Minsoy × Archer, Archer × Noir 1, Clark × Harosoy, and A81-356022 × PI468916. The JoinMap software package was used to combine the five maps into an integrated genetic map spanning 2,523.6 cM of Kosambi map distance across 20 linkage groups that contained 1,849 markers, including 1,015 SSRs, 709 RFLPs, 73 RAPDs, 24 classical traits, six AFLPs, ten isozymes, and 12 others. The number of new SSR markers added to each linkage group ranged from 12 to 29. In the integrated map, the ratio of SSR marker number to linkage group map distance did not differ among 18 of the 20 linkage groups. However, the same authors noted that the SSRs were not uniformly spaced over a linkage group, clusters of SSRs with very limited recombination were frequently present. These clusters of SSRs may be indicative of gene-rich regions of soybean, indicating the significant association of genes and SSRs. The creation of a high-density, integrated soybean linkage map with more precisely positioned markers would permit a better overall assessment of the distribution of SSR loci in the soybean genome. Genetic diversity of Asian soybean germplasm (Abe et al., 2003; Wang et al., 2006) as well as European soybean germplasm (Tavaud-Pirra et al., 2009) is studied by microsatellites. Sudaric et al (2008, 2009) evaluate the genetic diversity of the selected soybean germplasm using SSR markers, as well as to compare the effectiveness of breeding procedures with and without the use of genetic markers in parental selection. Based on SSR marker data and phenotypic data, an association was found between the agronomic performance of the derived lines and the genetic distance between the parental lines. Crosses between more diverse parents resulted in derived lines with greater values for grain yield (Table 1) and grain quality (Table 2) compared with the parents than crosses between similar parents. The results indicated on usefulness of genetic marker information in parental selection, contributing to breeding efficiency. Derived line (F4) Code of derived line

Female component

Male component

Deviation of Value

Female component

Male component

Small genetic distance between parental lines L-86-03

3.14

3.62

3.50

+ 0.36

- 0.12

L-104-03

3.14

4.20

3.65

+ 0.51*

- 0.55*

L-165-03

4.22

3.67

3.98

- 0.24

+ 0.31

L-224-03

4.22

4.14

4.20

- 0.02

0.06

Large genetic distance between parental lines L-23-03

3.28

3.66

3.92

+ 0.64*

+ 0.26

L-96-03

3.28

3.94

4.20

+ 0.92*

+ 0.26

L-115-03

4.09

3.89

4.52

+ 0.43*

+ 0.63*

L-193-03

4.09

3.67

4.38

+ 0.29

+ 0.71*

* signficant at P=0.05 according to F-test

Table 1. Grain yield (t/ha) of tested soybean lines (2007, Osijek, Croatia)

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Soybean - Molecular Aspects of Breeding

Code of derived line

Female component

Derived line (F4)

Male component

Deviation of Value

Female component

Male component

Small genetic distance between parental lines L-48-03

38.51

39.96

39.40

+ 0.89*

- 0.56*

L-94-03

38.51

40.50

39.56

+ 1.05*

- 0.94*

L-136-03

40.88

39.27

40.35

- 0.53*

+ 1.08*

L-212-03

40.88

40.52

40.62

- 0.26

+ 0.10

L-11-03

38.87

39.28

39.43

+ 0.56*

+ 0.15

L-65-03

38.87

39.61

39.75

+ 0.88*

+ 0.14

L-170-03

40.06

39.79

40.18

+ 0.12

+ 0.39*

L-205-03

40.06

40.16

40.46

+ 0.40*

+ 0.30

* signficant at P=0.05 according to F-test

Table 2. Protein content in grain (% in ADM) of tested soybean lines (2007, Osijek, Croatia) The SSR markers linked to the major QTL will be useful for marker-assisted selection in soybean-breeding programs (Funatsuki et al., 2005; Panthee et al., 2006). A set of SSR markers have been subjected to continuous development and utilization for high throughput molecular mapping in soybean (Akkaya et al., 1992, 1995; Narvel et al., 2000; Burnham et al., 2003; Shultz et al., 2007). 2.3.5 Amplified Fragment Length Polymorphism The use of Amplified Fragment Length Polymorphism (AFLP markers) is based on combining the use of restriction enzymes (endonucleases) and selective amplification with polymerase chain reaction. Polymorphism of genomic DNA is detected through the length of DNA fragments after its digestion with restriction enzymes and amplification in polymerase chain reaction. The amplification products are then separated on highly resolving sequencing gels and visualised using autoradiography. Where radio-labelled nucleotides are not used in the PCR step, fluorescent or silver staining techniques can be used to visualise the products (Ford-Lloyd & Painting, 1996). AFLP analysis is a highly sensitive, highly reproducible and widely applicable method for detecting polymorphisms throughout the genome. Disadvantages of AFLP system are: expensive, technically demanding, uses radioisotopes and problems in interpreting banding patterns. In soybean, less attention was focused on the development of AFLP markers than in other plant species, mostly because of the successful application of SSRs. The use of AFLPs in soybean started as late as mid 1990s (Vos et al., 1995). However, one of the largest available AFLP maps of any plant species was developed in soybean (Keim et al., 1997). This map has the total of 840 loci from which 650 are AFLPs, while the USDA/Iowa State map has 1004 loci, most of which are RFLPs and SSRs. In the development of AFLP map, authors used somewhat altered protocol according to Travis et al. (1996). The AFLP has proven very useful to saturate specific genomic regions using bulked segregate analysis or the

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69

comparison of near isogenic lines for a trait of interest because of the large amount of marker data that can be obtained with AFLP without the need for previous knowledge of DNA sequence (Muehlbauer et al., 1988, Cregan, 1999). Still, AFLP technologies are continuously being modified and perfected (Lin et al., 1999; Mano et al., 2001). 2.3.6 Single NucleotidePolymorphism Differences in individual DNA bases between homologous DNA fragments along with small insertions and deletions are collectively referred to as single-nucleotide polymorphism (SNP). SNPs can serve as genetic markers that can complement and greatly augment existing genetic linkage maps and are therefore useful as genetic markers in QTL analysis or other DNA marker based genetic analysis (Cregan, 1999). SNP is the most abundant source of DNA polymorphism in humans (Collins et al., 1998), while in plants, SNPs nature and frequency researches have just started to acquire significance (Gupta et al., 2001; Rafalski, 2002; Shoemaker et al., 2004). Many SNP detection methods have been developed, such as oligonucleotide ligation, denaturing high-performance liquid chromatography (DHPLC) and primer extension (Wu & Wallance 1989; Hoogendoorn et al. 1999; Pastinen et al. 2000; Wolford et al. 2000). In soybean, SNPs nature and frequency researches have intensified (Cahill, 2000, Zhu et al., 2003; Kim et al., 2004; Van et al., 2004, 2005), and thus are likely to have an important role in the future of soybean genome analyses and manipulation.

3. Genetic modification Tremendous progress in plant molecular biology over the last three decades has enabled development and use of techniques for manipulation with genetical structure of organisms, with the aim to ˝transfer˝ adequate genes and acquire desired combinational properties. Unlike traditional plant breeding, which involves the crossing of hundreds or thousands of genes, genetic transformation allows transfer of only one or a few desirable genes. This more precise technique allows plant breeders to develop crops with specific beneficial traits and without undesirable traits. Through traditional breeding methods, genes have been transferred from one individual to another with the aim of production individuals which clearly exhibit particular desirable traits. These crossing are usually between individuals of the same, or closely related species. The gene pool available for use, in traditional crossing, is thus limited to those genes present in individuals which can be induced to breed using natural crossing methods. The use of recombinant DNA technologies enables the movement of a single or a few genes within or across species boundaries to produce plants with new traits, transgenic plants. Also, it is possible to get rid of an undesirable trait by shutting down the ability of the cell to make the product specified by the gene (Konstantinov et al., 2002; Drinic Mladenovic et al., 2004). The processes involved in developing genetically modified plants are the following: identification and isolation of the desired gene, gene cloning, development of transgenes, gene transfer and introduction into breeding processes. The identification of a single gene is not sufficient. It is necessary to be acquainted with regulatory mechanisms of gene actions, as well as, with their secondary effects and

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interactions with other genes. Prior to this, it is necessary to identify an organism containing the desires gene in its genome (Konstantinov et al., 2002). Todorovska et al. (2010) has noted that the main limitation of this technology is the availability of preliminary knowledge about the role of a gene in determining a given trait and is, at present, only applicable for traits that are determined by one or a relatively small number of genes. Transgenic plants represent completely new genotypes (plant with novel traits). Therefore, in order to confirm expected phenotypic expression of the new trait, selection after the gene transfer is necessary, the same after conventional hybridization. Nevertheless, there still are many unknowns and disputes concerning transgenic plants from many different aspects: ethical, philosophical, religious, economic, ecological, sanitary, legal etc, and much time will still be needed to put transgenic plants in their rightful place with the help of scientific research (Keller & Huttter Carabias, 2001; Taylor, 2007). Legislative framework that regulates cultivation and commercialisation of transgenic plants, and the use of their products, is made, exists, and is being changed under the influence of science, business and national interests. According to the Cartagena Protocol on Biosafety (http://bch.cbd.int/protocol/) entered into force 2003 signed by 103 world countries and with 160 parties to Protocol by 2010, each country has the right to protect its biodiversity. Researches show that transgenic plants can transfer some of their features by pollen to related species that grow in the same area. Natural crossing between transgenic and conventional cultivars of the same species can occur in the same way, creating progeny that, beside desired traits, has novel genetic combinations with insufficiently known outcomes. Furthermore, it still isn't completely known how transgenic plants would behave if they escaped controlled cultivation and entered into the ecosystem. Their presence can affect the genome of some native forms of the same species that are considered ˝gene bank˝ of the certain area and are of unique significance for breeding. Congruently, 28 European countries signed Berlin Manifesto for GMO-free Regions and Biodiversity in Europe (www.gmo-freeregions.org/gmo-free-conference-2005/berlin-manifesto.html) in 2005, which points out that European regions have the right to determine their own ways of farming, producing and selling food, thus protecting the environment, landscapes, culture and heritage, their seed, their rural development and their future. This includes the right to decide about the use of genetically modified plants in agriculture and ecosystems. To date, genetic modifications were made on several important crops such as: soybean, corn, canola (rapeseed), sugar beet, potato, cotton, wheat, barley, rice and beans. One of the first important practical uses of genetic engineering in agriculture was development of glyphosate tolerant cultivars of soybean, corn, canola (rapeseed) and cotton (Moll, 1997). Glyphosate (N-(phosphonomethyl) glycine) is translocational, nonselective, post-emergence broad-spectrum herbicide. It's toxic effect on plants consists of inhibition of 5-enolpyruvyl shicimic acid-3-phosphate synthase (EPSPS) enzyme. EPSPS is very important in biosynthetic metabolism of aromatic amino acids – tryptophan, tyrosine and phenylalanine (Haslam, 1993). Glyphosate functions by occupying the binding site of phosphoenol pyruvate in EPSPS and blocking its activity, which in turn prevents aromatic amino acid production. Aromatic amino acids are very important for many biochemical processes such as: protein synthesis, cell membrane formation, protection against pathogens etc. Prevention of their biosynthesis leads to withering of plants. EPSPS is present in all plants (localised in chloroplasts or plastids), bacteria, fungi, but not in animals, which instead obtain aromatic amino acids from their diet. Researches done by Padgette et al. (1995, 1996) show that it is very difficult to accomplish significant endogenous tolerance to glyphosate in plant species

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with conventional breeding and induced mutations. Nevertheless, in 1983 glyphosate resistant CP4 gene was identified in Agrobacterium sp. strain CP4 (Padgette et al., 1996). Using biotechnology, gene was cloned and transferred into plants. As a result, new transgenic plants contained two types of genes in their genetic material: native genes for glyphosat-intolerant form of EPSPS and foreign, bacterial CP4 genes for glyphosat-resistant form of EPSPS. Inserted CP4 genes enabled plants to normally biosynthesize aromatic amino acids, so even after glyphosat application plants wouldn’t wilt. Greatest commercial success with glyphosate-tolerant (GT) cultivars was made in soybean. First GT soybean cultivars were named Roundup Ready (RR), after the commercial herbicide Roundup, which contained glyphosate as an active ingredient. Development of RR soybean cultivars enabled broad use of Roundup herbicide in arable crops, and thus very successful wide spectrum weed control in soybean. Before RR soybean cultivars, producers applied adequate herbicide regime, which was sometimes phytotoxic for soybean plants and/or left residues in the soil that were harmful for the following crop (Gianessi and Carpenter, 2000). Introduction of RR soybean into production resulted in reduction of herbicide treatment of crops. Single treatment with Roundup was sufficient which added to decreasing of production costs, and increasing of productivity. Commercialization of RR soybean started in 1996 in the USA. Production of RR cultivars had advantage over production of conventional cultivars from economic aspect, and thus they were rapidly accepted by American farmers. From 1996 to 1999, RR cultivars have been sown on 7, 17, 44 and 57% of total field area under soybean in USA, and from year 2000 to 2005, on 54, 68, 75, 81 and 87% (USDA, National Agric. Statistics Service). In 2009, GT soybeans are planted in nine countries covering 69,2 million hectares (52% of global biotech area of 134 mill ha) or 77% of total area planted with soybean (90 mil ha) (James, 2009). Results of the researches conducted by Minor (1998), Oplinger et al. (1998) and Elmore et al. (2001) showed that GT soybean cultivars didn’t have considerably better agronomic properties than conventional cultivars concerning seed yield, seed quality and resistance to diseases. The only advantage of GT cultivars is the cheaper production process because of the lower costs of weed control. In general, herbicide tolerance is considered the first generation of soybean fears obtained by biotechnology. To date, glyphosat-tolerant cultivars have had the greatest commercial success. Further researches are made with the aim to discover genetic resistance to other active ingredients in herbicides. The main goal of these soybean cultivars is reducing the use of pesticides and decreasing the number of applications, which would result in decreasing of production costs and increasing profit. Unlike the first generation GT soybean which was developed with gene gun technology, second generation (RReady2Yield™) was developed with more efficient and precise Agrobacterium insertion technology. Genetic mapping of soybean allowed yield enhancing regions of soybean DNA to be identified and in conjunction with advanced insertion and selection technology allowed the RReady2Yield™ gene (MON 89788) to be precisely inserted in one of the high yielding zones (http://www.monsanto.com/pdf/features/mon89788.pdf). The second generation RReady2Yield™, as a result of the linkage established between yield and glyphosate tolerance, offered significant increases in yield of 7 to 11% over the first generation. In 2009, RReady2Yield™ varieties of selected maturity classes, represented the first commercially approved product from a new wave of a whole new class of second generation biotech crop products were commercialized for the first time in a controlled launch in the USA and Canada on approximately 0.5 million hectares (James, 2009).

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The second generation fears put into soybean via biotechnology is increased oleic acid content (Kinney, 1996), increased lysine content (Falco et al., 1995) and achieved resistance to pests from Lepidoptera sp. by Bt (Bacillus thurigiensis) technology (Walker et al., 2002). The greatest commercial success in this generation was made by cultivars with high oleic acid content (HO cultivars). The first HO soybean lines (G94-1, G94-19, G168) were commercialized in 1998. The reasons behind their development are following. It is well known that soybean is the biggest resource of vegetable oil in the USA, and primary resource for production of biofuels. Average oil content in soybean seed is around 18%, and usual oil composition is: palmitic acid (11%), stearic acid (4%), oleic acid (22%), linoleic acid (53%) and linolenic acid (8%). Oils with high content of mono-unsaturated fatty acids (such as oleic acid) are especially important for processing industry, because they have bigger oxidative stability. This is why the researches were directed towards increasing oleic acid content in soybean oil. Mutagenesis increased oleic acid content to 30-65% (Takagi & Rahman, 1996), while genetic engineering increased it to more than 80% (Buhr et al., 2002). Third generation of transgenic soybean lines is being created in laboratories and for now they still haven’t been commercialized. Properties included in the researches are: enzymes (especially oxalate oxidase for the resistance to the disease Sclerotinia sclerotiorum), longchain fatty acids, vitamins, pharmaceutical ingredients, bioplastics, increased yield, drought and cold tolerance and many other benefits. Furthermore, genetic engineering was successfully used to eliminate allergenic proteins from soybean seed. Transgenic soybean lines with inactivated genes for main allergenic proteins are already being tested, and if they pass the tests we can expect their soon commercialization. Although on global level there are still controversies concerning transgenic plants, and researches demand large financial investments, further researches and technological development are continuous. Advance in functional genomics enables the discovery of increasing number of genes available for transformation, and with perfecting the methods of genetic engineering; new transgenic soybean cultivars with specific traits are being created.

4. Conclusion Biotechnology can be defined broadly as a set of tools that allows scientists to genetically characterize or improve living organisms. Several emerging technologies, such as molecular characterization and genetic transformation, are already being used extensively for the purpose of plant improvement. Tools provided by biotechnology will not replace classical breeding methods, but rather will help provide new discoveries and contribute to improved nutritional value and yield enhancement through greater resistance to disease, herbicides and abiotic factors. Plant biotechnology depends upon a number of laboratory procedures that have been developed recently to manipulate DNA and provide new genes of interest to the plant breeder. These procedures have resulted in crop plants that have great commercial value, and many companies are marketing genetically engineered crop varieties. In addition, biotechnology has allowed scientists, as never before, to expand their visions of designing new crop plants to serve humankind. In order to continue the development of gene technology in agriculture, the risks must be carefully analyzed and the plants monitored to detect possible problems. Glycine max (L.) Merr has the genetic diversity for differentiation, produces a balanced combination of protein, fat and carbohydrate to serve as a valuable food, feed, and bio-feedstock, inhabits cropping systems as a valuable contributor of nitrogen, and possesses other agronomical complementary traits. Given the coming

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advancements in biotechnology, the future of soybean will require the sound use of genetic resource within Glycine, adequate funding for research and development, and a clear vision of the opportunities that lie ahead. Scientific discoveries in the area of structural and functional plant genomics would lead to production of new soybean varieties with advanced nutritive and agronomic properties, created by combining conventional breeding methods and biotechnology tools. Based on the availability and combination of conventional and molecular technologies, a substantial increase in the rate of genetic gain for economically important soybean traits can be predicted in the next decade.

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Lin, J. J.; Ma, J. & Kuo, J. (1999). Chemiluminescent detection of AFLP markers. BioTechniques, 26, 344-348, ISSN 0736-6205. Liu, B.H. (1998). Statistical Genomics. Linkage, mapping and QTL Analysis, CRC Press, ISBN 08493-3166-8, Boca Raton, USA. Malik, F.; Qureshi, A.; Ashraf, M.; Khan, M. & Javed, A. (2009): Evaluation of genetic diversity in soybean (Glycine max) lines using seed protein electrophoresis. Aust. J. Crop Sci., 3 (2) 107-112. Mano, Y.; Kawasaki, S.; Takaiwa, F. & Komatsuda, T. (2001). Construction of a genetic map of barley (Hordeum vulgare L.) cross 'Azumamugi' x 'Kanto Nakate Gold' using a simple and efficient amplified fragment length polymorphism system. Genome, 44, 284-292, ISSN 0831-2796. Minor, H. 1998. Performance of GMOs vs. Traditional varieties: A southern perspective. Proceedings of the 28th Soybean Seed Research Conf., pp. 1-9., Chicago, Am. Seed Trade Assoc., Washington, USA. Miroslav, K. & Jiri, L. (1996). Identification of soybean cultivars through isozymes. Soybean Genet. Newsl., 23, 89-91. Mladenovic Drinic, S.; Nikolic, A. & Peric, V. (2008). Cluster Analysis of Soybean Genotypes Based on RAPD Markers. Proceedings of the 43rd Croatian and 3rd International Symposium on Agriculture, pp. 367-370, ISBN 978-953-6135-67-7, Opatija, february 2008, Faculty of Agronomy, Zagreb , Croatia. Mohan, M.; Nair, S.; Bhagwat, A.; Krishna, T. G. & Yano, M. (1997). Genome mapping, molecular markers and marker assisted selection in crop plants. Mol. Breed., 3 (2), 87-103, ISSN 1380-3743. Moll, S. (1997). Commercial experience and benefits from glyphosate tolerant crops. Proceedings of the Brighton Crop Protection Conference: weeds 3, 931-940, November 1997, Brighton, UK. Morgante, M. & Olivieri, A. M. (1993). PCR-amplified microsatellites as markers in plant genetics. Plant J., 3, 175-182, ISSN 0960-7412. Muehlbauer, G.J.; Specht, J.E.; Thomas-Comton, M.A.; Staswick, P.E. & Bernard, R.L. (1988). Near isogenic lines - a potential source in the integration of conventional and molecular marker linkage maps. Crop Sci., 28, 729-735, ISSN 0011-183x. Narvel, J. M.; Chu, W. C.; Fehr, W. R.; Cregan, P. B. & Shoemaker, R. (2000): Development of multiplex sets of simple sequence repeat DNA markers covering the soybean genome. Mol. Breed., 6, 175–183, ISSN 1380-3743. Nikolic, A.; Drinic Mladenovic, S. & Srebric, M. (2004). Genetic diversity of soybean genotypes using molecular markers. Proceedings of ESNA XXXIV annual meeting, pp. 274-277., Novi Sad. Nikolic, A.; Srebric, M. & Drinic Mladenovic, S. (2005). Genetic similarity of soybean genotypes revealed by seed protein. Plant breeding and seed production, 11, 1-4, ISSN 0354-5881 Nikolic, A.; Drinic Mladenovic, S. & Srebric, M. (2007). Characterisation of soybean genotypes using genetic markers. J. Sci. Agric. Res., 68, 241, 5-20, ISSN 0354-5695.

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Oplinger, E.S.; Martinka, M.J. & Schmitz, K.A. (1998). Performance of transgenic soybeans: Northern United States. Proceedings of the 28th Soybean Seed Research Conf., Chicago, pp. 10-14, Am. Seed Trade Assoc., Washington, USA. Orf, J. H.; Diers, B.W. & Boerma, H.R. (2004). Genetic Improvement: Conventional and Molecular-based Strategies. In: Soybeans: Improvement, Production and Uses, Shibles R.M. & Specht, J.E. (Eds.), 417-450, ASA, CSSA, SSSA, ISBN 0-89118-154-7, Wisconsin, USA. Padgette, S.R.; Kolacz, K.H.; Delannay, X.; Re, D.B.; LaVallee, B.J.; Tinius, C.N.; Rhodes, W.K.; Otero, Y.I.; Barry, G.F.; Eichholtz, D.A.; Peschke, V.M.; Nida, D.L.; Taylor, N.B. & Kishore, G.M. (1995). Development, identification and characterization of a glyphosate-tolerant soybean line. Crop Sci. 35, 1451-1461, ISSN 0011183x. Padgette, S.R.; Re, D.B.; Barry, G.F.; Eichholtz, D.E.; Dellannay, X.; Fuchs, R.L.; Kishore, G.M. & Faley, R.T. (1996). New weed control opportunities: Development of soybeans with a Roundup Ready gene. In: Herbicide resistant crops: Agricultural, economic, environmental, regulatory and technical aspects, Duke, S.O. (Ed.), 53-84, CRC Press, ISBN 1-56670-045-0, Boca Raton, FL. Panthee, D. R.; Pantalon, V. R.; Saxton, A. M.; West, D. R. & Sams, C. E. (2006). Quantitative trait loci for agronomic traits in soybean. Plant Breed. 126, 51-57, ISSN 0179-9541. Pastinen, T.; Raitio, M.; Lindroos, K.; Tainola, P.; Peltonen, L. & Syvanen, A.C. (2000). A system for specific, high-throughput genotyping by allele-specific primer extension on microarrays. Genet Res, 10,1031–1042, ISSN 0016-6723. Peric, V.; Eric, I.; Drinic Mladenovic, S. & Srebric, M. (2006). Determination of Genetic Diversity in Soybean Genotypes by RAPD markers. J. Sci. Agric. Res., 67, 1, 33-39, ISSN 0354-5695. Peric, V.; Nikolic, A. & Drinic Mladenovic, S. (2008a). RAPD marker based cluster analysis of soybean genotypes. Book of abstracts of the Fifth Scientific-Research Symposium on Breeding and Seed Production of the Serbian Association of Plant Breeding and Seed Producers, pp. 109, Vrnjacka Banja, Serbian Association of Plant Breeding and Seed Producers, Serbia, ISBN 978-86-80383-06-4. Peric, V.; Drinic Mladenovic S., & Srebric, M. (2008b). Genetic markers in estimating similarity of soybean parental genotypes and predicting performance of progeny. Book of abstracts of the 43rd Croatian and 3rd International Symposium on Agriculture, pp. 110, ISBN 978-953-6135-67-7, Opatija, February 2008, Faculty of Agronomy, Zagreb, Croatia. Prabhu, R. R. & Gresshoff, P. M. (1994). Inheritance of polymorphic markers generated by DNA amplification fingerprinting and their use as genetic markers in soybean. Plant. Mol. Biol., 26, 105-116, ISSN 0167-4412. Rafalski, A. & Tingey, S. (1993). RFLP map of soybean (Glycine max). In: Genetic maps: Locus maps of complex genomes, O'Brien, S. J. (Ed.), 6.149-6.156, Cold Spring Harbor Lab. Press, ISBN 978087963436, New York, USA. Rafalski, A. (2002). Applications of single nucleotide polymorphisms in crop genetics. Curr Opin Plant Biol , 5,94–100, ISSN 1369-5266. Rajcan, I.; Hou, G. & Weir, A. D. (2005). Advances in Breeding Of Seed-Quality Traits in Soybean, In: Genetic and Production Innovations in Field Crop Technology: New

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Developments in Theory and Practice, Manjit S. Kang (Ed.), 145-174, Food Products Press, ISBN-13:978-1-56022-123-4, New York, USA. Shoemaker, R. C. & Olson, T. C. (1993). Molecular linkage map of soybean (Glycine max L. Merr.). In: Genetic maps: Locus maps of complex genomes, O'Brien, S. J. (Ed.), 6.1316.138, Cold Spring Harbor Lab. Press, ISBN 978087963435, New York, USA. Shoemaker, R.C. & Specht, J.E. (1995). Intergration of the soybean molecular and classical genetic linkage groups. Crop Sci., 35, 436-446, ISSN 0011-183x. Shoemaker, R. C.; Cregan, P.B. & Vodkin, L.O. (2004). Soybean Genomics. In: Soybeans: Improvement, Production and Uses, Boerma, H.R. & Specht, J.E. (Eds.), 235-263, ASA, CSSA, SSSA, ISBN 0-89118-154-7, Wisconsin, USA. Shultz, J. L.; Ray J.; Smith J. & Mengistu, A. (2007). A soybean mapping population specific to the early soybean production System. DNA Seq., 18 (2), 104–111, ISSN 1042-5179. Song, Q.J.; Marek, L.F.; Shoemaker, R.C.; Lark, K.G.; Concibido, V.C.; Delannay, X.; Specht, J.E. & Cregan, P.B. (2004). A new intergrated genetic linkage map of soybean. Theoretical and Applied Genetics, 109, 1, 122-128, ISSN 0040-5752. Sonnewald, U. & Herbers, K. (2001). Plant biotechnology: Methods, Goals and Achievements. In: Crop Science: Progress and Prospects, Nosberger, J; Geiger, H.H. & Struik, P.C. (Eds.), 329-350, CABI Publishing, ISBN 0 85199530 6, New York, USA. Southern, E.M. (1975). Detection of specific sequences among DNA fragments separated by gel electrophoresis. J. Mol. Biol., 98, 503-517, ISSN 0022-2836. Specht, J.E.; Hume, D.J. & Kumudini, S.V. (1999). Soybean yield-potential – A genetic and physiological perspective. Crop Science, 39, 1560-1570, ISSN 0011-183x. Srebric, M.; Kovacevic, D.; Peric, V. & Mladenovic Drinic, S. (2010). Identification of Kunitzfree soybean lines. Book of abstract of the sixth scientific-research symposium on breeding and seed production, pp. 31, ISBN 978-86-901937-5-2, May 17-21, 2010, Vršac. Sudaric, A.; Vrataric, M.; Rajcan, I.; Duvnjak, T. & Volenik M. (2008). Application of molecular markers in parental selection in soybean. Acta Agron. Hung., 56, 4, 393398. Sudaric, A.; Vrataric M. & Sudar, R. (2009). Genetic improvement of soybean by modern breeding strategies in region of the Eastern Croatia. Proceedings of World Soybean Research Conference VIII, pp. 54, Beijing, august 2009, Chinese Academy of Agricultural Sciences, Beijing , China. Sudaric, A.; Vrataric, M.; Mladenovic Drinic, S. & Matosa M. (2010). Biotechnology in soybean breeding. Genetika, 42, 1, 91-102, ISSN 0534-0012. Takagi, Y. & Rahman, S.M. (1996). Inheritance of high oleic acid content in the seed oil of soybean mutant M23. Theor. Appl. Genet., 92, 179-182, ISSN 0040-5752. Tavaud-Pirra M.; Sartre P.; Nelson R.; Santoni S.; Texier, N. & Roumet, P. (2009). Genetic Diversity in a Soybean Collection. Crop Sci., 49, 895–902, ISSN 0011-183x. Taylor, I.E.P. (2007). Genetically Enginered Crops Interim Policies, Uncertain Legislation. Haworth Food & Agricultural Products Press, ISBN 13: 978-1-56022-988-9, Binghamton, USA. Todorovska, E.; Atanassov, A. & Vassilev, D. (2010). From genetics to genomics in plants and animals. Genetika, 42, 1, 177-194, ISSN 0534-0012.

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Travis, S.E.; Maschinski, J. & Keim, P. (1996). An analysis of genetic variation in Astragalus cremnophylax var. Cremnophylax, a critically endangered plant, using AFLP markers. Mol. Ecol., 5, 735-745, ISSN 0962-1083. Ustun, A.; Allen, F.L. & English, B.C. (2001). Genetic progress in soybean of the U.S. Midsouth. Crop Sci., 41, 993-998, ISSN 0011-183x. Van, K.; Hwang, E. Y.; Kim, M.Y.; Kim, Y.H.; Cho, Y.I.; Cregan, P.B. & Lee, S.H. (2004). Discovery of single nucleotide polymorphisms in soybean using primers designed from ESTs. Euphytica,139, 147–157, ISSN 0014-2336. Van, K.; Hwang, E.-Y.; Kim, M. Y.; Park, H. J.; Lee, S.-H. & Cregan, P. B. (2005). Discovery of SNPs in Soybean Genotypes Frequently Used as the Parents of Mapping Populations in the United States and Korea. Journal of Heredity, 96, 5, 529-535, ISSN 0022-1503. Verma, D. P. S. & Shoemaker, R.C. (1996). Soybean: Genetics, Molecular Biology and Biotechnology. Biotechnology in Agriculture No. 14. CAB International, ISBN 0 85198 863 6, Wallingford, UK. Vos, P.; Hogers, R.; Bleeker, M.; Rijans, M.; Van der Lee, T.; Hornes, M.; Frijters, A.; Pot, J.; Peleman, J.; Kuiper, M., & Zabeau, M. (1995). AFLP: A new technique for DNA fingerprinting. Nucleic Acids Res., 23, 4407-4414, ISSN 0305-1048. Vratarić, M. & Sudarić, A. (2008). Soja Glycine max (L.) Merr., Agricultural Institute Osijek, ISBN 978-953-97114-7-2, Osijek, Hrvatska. Walker, D.R.; Boerma, H.R.; All, J.N. & Parrott, W.A. (2002). Combining cry1Ac with QTL alleles from PI 229358 to improve soybean resistance to lepidopteran pests. Molecular Breeding, 9, 43-51, ISSN 1380-3743. Wang, L. X., Guan R., Zhangxiong L., Chang, R. & Qiu, L. (2006). Genetic diversity of chinese cultivated soybean revealed by SSR markers. Crop Sci., 46,1032-1038, ISSN 0011-183x. Weber, D. & Helentjaris, T. (1989): Mapping RFLP loci in maize using B-A translocations. Genetics, 121, 583-590, ISSN 0016-6731. Welsh, J. & McClelland, M. (1990). Fingerprinting genomes using PCR with arbitrary primers. Nucleic Acids Research, 18, 7213-7218, ISSN 0305-1048. Wilcox, J.R. (2001). Sixty years of improvement in publicly developed elite soybean lines. Crop Sci., 41, 1711-176, ISSN 0016-6731. Williams, J.G.K.; Kubelik, A.R.; Livak, K.J.; Rafalski, J.A. & Tingey, S.V. (1990). DNA polymorphisms amplified by arbitrary primers are useful as genetic markers. Nucleic Acids Research, 18, 6531-6535, ISSN 0305-1048. Wolford, J.K.; Blunt, D.; Ballecer, C. & Prochazka, M. (2000). High-throughput SNP detection by using DNA pooling and denaturing high performance liquid chromatography (DHPLC). Hum Genet, 107, 83–487, ISSN 0340-6717. Wu, D.Y. & Wallance, R.B. (1989). The ligation amplification reaction (LAR)-amplification of specific DNA sequences using sequential rounds of template-dependent ligation. Genomics, 4, 460–569, ISSN 0888-7543. Zhu, Y.L.; Song, Q.J.; Hyten, D. L.; Van Tassell, C. P.; Matukumalli, L.K.; Grimm, D. R.; Hyatt, S. M.; Fickus, E. W.; Young, N. D. & Cregan, P. B. (2003). Single nucleotide polymorphisms (SNPs) in soybean. Genetics, 163, 3, 1123-1134, ISSN 0016-6731.

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www.fao.org http://bch.cbd.int/protocol/ www.gmo-free-regions.org/gmo-free-conference-2005/berlin-manifesto.html www.monsanto.com/pdf/features/mon89788.pdf

5 Integration of Major QTLs of Important Agronomic Traits in Soybean Guohua Hu, Qingshan Chen, Chunyan Liu, Hongwei Jiang, Jialin Wang and Zhaoming Qi Northeast Agricultural University, Land Reclamation Research & Breeding Centre of Heilongjiang P. R. China

1. Introduction Soybean [Glycine max (L.) Merr.] is one of the most important crops in the world, accounting for 48% of the world market in oil crops, and is used mostly for animal feed and for oil production. In soybean, most agronomic traits, such as abiotic stress, biological stress, protein concentration, oil content and yield, are quantitative traits controlled by multiple genes. Moreover, genotypic expression of the phenotype is environmentally dependent, which affected QTLs detection greatly. QTLs detection was also affected by marker sets, experimental design, mapping populations, and statistical methods, which result in the location of QTLs mapped by the same marker differently. From the first publication of quantitative trait locus (QTL) of soybean using molecular markers (Paterson et al. 1988), numerous QTLs underlying different traits of soybean have been identified in different genetic backgrounds and environments. However, few identical QTLs were identified in same experimental population or same environment by different researchers or in different years (Orf 1999; Mansur 1993, 1996). All of these interfered in QTLs to improve oil content of soybean by marker-assisted selection (MAS). Integrated QTL analysis can facilitate the identification of “real” QTL and has attracted a great deal of attention. Recently, the International Maize and Wheat Improvement Center put forward a proposal to find universal QTLs by building a consensus map. This proposal offers a basis for QTL analysis and marker-assisted selection (MAS). Meta-analysis method led to about twofold increase in precision in the estimates of QTL position compared to the most precise initial QTL position within the corresponding region. In this chapter, QTL meta-analysis for major agronomic traits in soybean was performed for the first time. Published QTLs were collected, a consensus map of published maps with a reference map was created, consensus QTLs were acquired by the meta-analysis approach, genes were mined using bioinformatics tools, and markers of consensus QTLs with high effects and small CIs were provided for MAS.

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2. Collection of important agronomic traits QTLs 2.1 Collection of seed quality QTLs 2.1.1 Oil content QTLs Oil and protein concentration account for about 60% of dry soybeans by weight, protein at 40% and oil at 20%. The 130 QTLs for soybean oil content published in the last 20 years were No. of QTL

Parent 1

11

Charleston

10

Zhong Dou 29

4

Charleston

2

01-042 (G.max)

1

Wan 82-178

3

He Feng 25

2

Ke Xin 3

2 4 3 3 6

Sui Nong 14 Jin 23 N87-984-16 BSR 101 Essex

4

Wan 82-178

4

Charleston

3

Zheng 92116

1

Ke Feng 1

1

A3733

3

Ke Feng 1

2

Ke Feng 1

3 5 1 1 2 3 1 1

Ma.Belle Minsoy A81-356022 Parker Archer Archer Essex Minsoy

Parent 2 Dong nong594 Zhong Dou 32 Dong nong594 KT156(G.soja) Tong Shan Bo Pi Xin Min 6 Zhong Huang 20 Sui Nong 20 Hui Bu-Zhi TN93-99 LG82-8379 Williams Tong Shan Bo Pi Dong nong594 Shang 951099 Nan Nong1138-2 PI437088A Nan Nong1138-2 Nan Nong1138-2 Proto Noir 1 PI468916 PI468916 Minsoy Noir 1 Peking Noir 1

Population Analysis Population Size Method type CIM RIL 154 MIM

Reference Shan,2008

255

CIM

RIL

Wang,2008

154

CIM

RIL

Chen,2007

120

CIM

F2

Zhang,2008

133

CIM

RIL

Xu,2007

122

CIM

RIL

Lv,2006

192

CIM

F4

Zhu,2006

94 474 101 167 131

CIM CIM ANOVA ANOVA CIM

F2 RIL RIL RIL RIL

Zhu,2006 Liang,2005 Panthee,2005 Kabelka,2004 Hyten,2004

133

CIM

RIL

Li,2004

154

CIM

RIL

Zhang ZC,2004

105

IM

F2:3

Yang,2004

184

CIM

RIL

Zhang WK,2004

76

ANOVA

RIL

Chung,2003

201

CIM

RIL

Wang,2001

201

IM

RIL

Wu,2001

82 236 98 100 233 240 200 240

ANOVA ANOVA IM IM IM IM ANOVA IM

F2 RIL BC3F4 BC3F4 RIL RIL F2:3 RIL

Csanadi,2001 Specht,2001 Sebolt,2000 Sebolt,2000 Orf,1999 Orf,1999 Orf,1999 Orf,1999

83

Integration of Major QTLs of Important Agronomic Traits in Soybean

1 1 3 3 3 1 5 6 5 2 9 5

M82-806 M84-49 A87-29601 C1763 C1763 M81-382 PI97100 Young Minsoy Minsoy A81-356022 Misuzudaiz u

HHP Sturdy CX1039-99 CXl159-49 CX1039-99 PI 423.949 Coker237 PI416937 Noir 1 Noir 1 PI 468916 Moshidou Gong 503

71 92 100 89 83 81 111 120 284 284 60 -

ANOVA ANOVA ANOVA ANOVA ANOVA ANOVA ANOVA ANOVA ANOVA IM ANOVA ANOVA IM

F2:5 F2:5 F2:5 F2:5 F2:5 F2:5 F2 F4 RIL RIL F2:3

Brummer,1997 Brummer,1997 Brummer,1997 Brummer,1997 Brummer,1997 Brummer,1997 Lee,1996 Lee,1996 Mansur,1996 Mansur,1993 Diers,1992

-

Tajuddin,2003

Total:130 Table 1. Information of oil content QTLs collected from Soybase and 28 published articles (Table 1). Mansur (1996) mapped five oil QTLs of soybean by the population Minsoy and Noir 1 with the ANOVA method. Sebolt (2000) mapped a oil QTLs of soybean by the population A81-356022 and PI 468916 with the Interval Mapping (IM) method. Hyten (2004) mapped six oil QTLs of soybean by the population Essex and Williams with the Composite Interval Mapping (CIM) method. Shan (2008) first reported the interaction between QTLs and environments with RIL population, crossed by Charleston and Dongnong594, with CIM and Multiple Interval Mapping(MIM) method. The population used for above research involved RIL, F2, F4, F2:3, F2:5, and BC3F4. Information on interval mapping (IM), composite interval mapping (CIM), multiple interval mapping (MIM), and the ANOVA analysis method was obtained. 1 to 11 QTLs were mapped from different papers. 2.1.2 Protein concentration QTLs In the former researches, QTLs come from the 18 papers and soybase website listed in Table 2. Diers (1992) mapped eight protein QTLs of soybean by the population A81-356022 and PI 468916 with the ANVOA method. Furthermore, Diers (2000) reanalyzed and mapped a protein QTLs of soybean by the population A81-356022 and PI 468916 with the IM method, so the new QTLs were used in this study. In several cases, the same populations had been used by different research in independent experiments. For the Minsoy × Noir 1 population, Mansur (1996) mapped three protein QTLs of soybean with the ANVOA method, Orf (1999) mapped two protein QTLs of soybean with the IM method, Specht (2001) mapped five protein QTLs of soybean with the ANVOA method. For the Charleston × Dongnong 594 population, QTL × Environment effects were developed (Shan Dapeng et al., 2009), and used to reanalyze the new phenotypic data (Chen Qingshan et al., 2007; Zhang Zhongchen et al., 2004). The set of 107 seed protein concentration QTL (Table 2) was obtained from 21 populations, varying in size from 60 to 284. The analytical methods used to predict the QTL included ANOVA and both simple and composite interval mapping. The population type included RILs, F2, F2:3, F4, F2:5, and BC3F4. A few QTLs with a LOD score below 2.0 were discarded from the analysis to reduce the risk of including false positives.

84

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Populatio Analysis Populatio QTL Parent1 Parent2 Number n Size Method n Type 10 Charleston Dongnong 594 154 CIM RIL 5 Charleston Dongnong 594 154 CIM RIL 2 Charleston Dongnong 594 154 CIM RIL Zheng 3 Shang 951099 105 IM F2:3 92116 Nannong 201 IM RIL 3 Kefeng 1 1138-2 1 A3733 PI437088A 76 ANOVA RIL 1 A81-356022 PI468916 98 IM BC3F4 1 Parker PI468916 100 IM BC3F4 1 M82-806 HHP 71 ANOVA F2:5 1 M84-49 Sturdy 92 ANOVA F2:5 2 McCall PI 445.815 92 ANOVA F2:5 2 A87-29601 CX1039-99 100 ANOVA F2:5 3 C1763 CXl159-49 89 ANOVA F2:5 2 C1763 CX1039-99 83 ANOVA F2:5 1 LN83-2356 PI 360.843 69 ANOVA F2:5 2 Archer Minsoy 233 IM RIL 1 Archer Noir 1 240 IM RIL 2 Minsoy Noir 1 240 IM RIL 10 BSR 101 LG82-8379 167 ANOVA RIL 6 PI97100 Coker237 111 ANOVA F2 13 Young PI416937 120 ANOVA F4 6 Essex Williams 131 CIM RIL 2 Essex Peking 200 ANOVA F2:3 4 Ma.Belle Proto 82 ANOVA F2 ANOVA， Misuzudaiz Moshidou 6 u Gong 503 IM 1 N87-984-16 TN93-99 101 ANOVA RIL 5 Minsoy Noir 1 236 ANOVA RIL 3 Minsoy Noir 1 284 ANOVA RIL 8 A81-356022 PI 468916 60 ANOVA F2:3 Total： 107

Reference Shan,2009 Chen,2007 Zhang,2004 Yang,2004 Wu,2001 Chung,2003 Sebolt,2000 Sebolt,2000 Brummer,1997 Brummer,1997 Brummer,1997 Brummer,1997 Brummer,1997 Brummer,1997 Brummer,1997 Orf,1999 Orf,1999 Orf,1999 Kabelka,2004 Lee,1996 Lee,1996 Hyten,2004 Qiu,1999 Csanadi,2001 Tajuddin,2003 Panthee,2005 Specht,2001 Mansur,1996 Diers,1992

Table 2. The origin of the set of seed protein concentration QTL included in the metaanalysis (The table details the number of QTL mapped in each population, along with the identity of the parents, the size and the type of each population) 2.1.3 Fatty acid QTLs 83 QTLs come from the 8 papers and soybase website were listed in Table 3. Among them, 16 QTLs were related with palmitic acid content; 11 QTLs related with stearic acid content; 32 QTLs related with oleic acid content; 10 QTLs related with linoleic acid content; and 14

Integration of Major QTLs of Important Agronomic Traits in Soybean

85

QTLs related with linolenic acid content. In 2007, Lee summerized 60 QTLs related with fatty acid from 2002 to 2008. Some RFLP markers, not appear in the soybean public genetic map, were discarded. The analytical methods used to predict the QTL included ANOVA, IM, CIM, and MIM. The population type included RILs, F2, F2:3, F4, RIL, and BC1F1. Trait Palmitic acid Palmitic acid Palmitic acid Palmitic acid Palmitic acid Stearic acid Stearic acid Stearic acid Stearic acid Oleic acid Oleic acid Oleic acid Oleic acid Linoleic acid Linoleic acid Linoleic acid Linoleic acid Linolenic acid Linolenic acid Linolenic acid

Marker LG Population Type Data analysis Satt537 D1b RIL CIM Satt330-Sat_155 I BC1F1 CIM MIM Satt175 M F2 ANOVA Sat_132 O RIL CIM ANVOA Satt684 A1 F4 SAS Sat_245-Satt373 L BC1F1 CIM MIM Satt249 J RIL CIM Satt474 B2 F2 BSA Sat_090 F RIL CIM & ANVOA Satt143 L F2:3 CIM Sat_356-Satt615 D2 BC1F1 CIM & MIM Satt263 E RIL CIM Satt163 G RIL CIM ANVOA Satt349 K RIL CIM & ANVOA Satt166-Satt156 L BC1F1 CIM & MIM Satt235 G RIL CIM Sat_274 O RIL IM Satt579-Satt600 D1b BC1F1 CIM & MIM Satt185 E RIL CIM Satt349 K RIL CIM & ANVOA

Reference Panthee,2006 Zheng, 2005 Li,2002 Yarmilla,2006 Andrea,2007 Zheng, 2005 Panthee,2006 Spencer,2004 Yarmilla,2006 Maria,2008 Zheng, 2005 Panthee,2006a Yarmilla,2006 Yarmilla,2006 Zheng, 2005 Panthee,2006 Masayuki,2008 Zheng, 2005 Panthee,2006 Yarmilla,2007

Table 3. Reported information of fatty acid QTLs in soybean 2.1.4 Amino acid content QTLs For the QTL information of amino acid content, only 3 researches involved (Panthee, 2006b, 2006c; Bingchang Zhuang, 2000). In total, 111 QTLs were mapped with RIL population. 2.1.5 Isoflavone content QTLs The reseach of isoflavone content was very few. 70 QTLs were mapped in 3 papers (Kassem, 2004; Meksem, 2001a; Guoliang Zeng, 2007) by RIL population. 2.2 Collection of biotic stress resistance QTLs 2.2.1 Fungal disease resistance QTLs Diseases caused by fungal pathogens account for approximately 50% of all soybean disease losses around the world. Conflicting results of fungal disease resistance QTLs from different populations often occurred. 107 QTLs in recent years of fungal disease resistance content were collected from 23 papers (Table 4). Only 1 to 5 QTLs were obtained in most researches. These QTLs had been mapped on the soybean linkage groups (LGs). not all fungal disease resistance content QTLs were collected because of some QTLs could not be projected to the reference map.

86

Soybean - Molecular Aspects of Breeding

QTL number

Population-type

Name of fungal disease

Reference

5

F2

Soybean rust

Alexandre, 2008

2

RIL

Brown stem rot

Bachman, 2001

8

RIL

Phytophthora root rot

Burnham, 2003

3

RIL

Phytophthora root rot

Weng, 2007

2

RIL

Asian soybean rust

Danielle, 2008

5

RIL

Sclerotinia stem rot, White mold

Guo, 2008

1

NIL

Soybean rust

Hyten, 2007

5

RIL

Sudden death syndrome

Kazi, 2008

2

RIL

Asian soybean rust

Monteros, 2007

1

F2

Frogeye leaf spot

Mian, 1999

34

RIL

Sclerotinia stem rot, White mold

Arahana, 2001

1

RIL

Frogeye leaf spot

Zhang, 2004

3

RIL

Sudden death syndrome

Njiti, 2002

10

RIL

Brown stem rot

Patzoldt, 2005a

6

RIL

Rhizoctonia root and Hypocotyl Zhao, 2005 rot

5

F2

Sudden death syndrome

Austeclinio, 2007

2

RIL

Phytophthora root rot

Han, 2008

2

RIL

Brown stem rot

Lewers, 1999

1

NIL

Brown stem rot

Patzoldt, 2005b

4

RIL

Sudden death syndrome

Iqbal, 2001

2

NIL

Sudden death syndrome

Meksem, 1999

1

RIL

Sudden death syndrome

Bell-Johnson, 1999

2

F2

Phomopsis seed decay

Berger, 1999

Total: 107 Table 4. QTLs information of fungal disease resistance in soybean 2.2.2 Insect resistance QTLs 81 QTLs from the 15 papers and soybase website were listed in Table 5. Among them, 12 QTLs were related with soybean aphid resistance, 42 QTLs related with corn ear worm(CEW) resistance, 11 QTLs related with common cut worm(CCW) resistance, and 16 QTL related with Mexican bean beetle and soybean pod borer resistance. The average interval of all above QTLs were 15 cM. The analytical methods used to predict the major site included BSA, ANOVA, IM, CIM, and MIM. The population type included RILs, F2, F2:3, F3:4, RIL, and BC6 population.

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Integration of Major QTLs of Important Agronomic Traits in Soybean

Name of insectresistance

QTL number

Populationtype

Soybean aphid

2

F2:3

Soybean aphid

2

F2, F2:3

Soybean aphid

8

Analysis Method

Reference

BSA

Li,2007

BSA

Mian,2008

F3:4

CIM, MIM, ANOVA

Zhang,2009

CCW

2

F2

CIM

Kunihiko,2005

CCW

2

RIL

CIM

Liu,2005

CCW

3

RIL

CIM

Fu,2007

CCW

4

F2

CIM

Kunihiko,2007

CEW

2

F2:3

IM

Rector,2000

CEW

2

F2

ANOVA, IM

Rector,1998

CEW

10

RIL

SAS, IM

Terry, 1999

CEW

20

RIL

IM

Terry, 2000

CEW

5

F2:3

IM

Narvel, 2001

CEW, SBL

3

BC6F2:3

ANOVA

Zhu, 2006

Mexican bean beetle

1

F2:3

ANOVA

Boerma, 2005

Soybean pod borer

15

RIL

CIM

Zhao, 2008

Total

81

Note: CIM, composite interval mapping; BSA, bulk segregant analysis; IM, Interval mapping; ANOVA, Analysis of variance; CCW, common cut worm; CEW, corn ear worm; SBL, Soybean looper

Table 5. QTLs information of insect-resistance in soybean 2.2.3 Soybean cyst nematode resistance QTLs Soybean cyst nematode is a very serous disease, reducing yields by as much as 75 percent. Many research was focus on the major QTL mapping. 135 QTLs underlying SCN No.1, 2, 3, 4, 5, 6, and 14 resistance from 23 papers and soybase website were listed in Table 6. For different research, 1 to 19 QTLs were analyzed. The analytical methods included GLM, BSA, ANOVA, IM, and CIM. The population type included RILs, F2, F2:3, F4, RIL, NIL, and BC. 2.3 Collection of yield trait QTLs 2.3.1 100-Seed weight QTLs Soybean seed size is determined by the genetics of the variety and the environment where the seed was produced. In America, seed size was described as seeds per pound, but in China, seed size was often defined as 100-seed weight. So the research of QTL mapping for 100-seed weight was mainly in China. Among them, the population crossed by Kefeng 1 and Nannong 1138-2 were used for 3 times. The analytical methods included IM and CIM. The population type included RILs and F2. In total, 78 QTLs were collected(Table 5).

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Soybean - Molecular Aspects of Breeding

QTL number 10 17 4 1 9 7 4 19 11 1 3 1 4 12 1 3 6 1 9 3 1 5 3 Total

Population-type F2:3 F2 F4 RIL F2:3 F2:3 BC3F2:3, F2:3 F2:3 BC1F2, BC1F4 F2 RIL RIL, NIL F2 F2, BC1F2 F2:3 RIL F2:3 NIL F2:3 F2:3 Population F2:3 F2:3, RIL 135

Analysis method CIM GLM CIM GLM CIM GLM IM GLM CIM,MIM BSA BSA ANOVA BSA & CIM CIM Stepwise regression CIM ANOVA ANOVA GLM GLM GLM GLM ANOVA

References Guo, 2006a Kabelka, 2005 Glover, 2004 Prabhu, 1999 Guo, 2006b Yue, 2001a Schuster, 2001 Yue, 2001b Lu weiguo, 2005 Wang hui, 2007 Li Yongchun, 2007 Meksem, 2001b Meng Xi, 2008 Wang, 2001 Vierling, 1996 Ferdous，2006 Qiu, 1999 Meksem, 1999 Brucker, 2005 Concibido, 1994 Mahalingam, 1995 Heer, 1998 Concibido, 1996

Table 6. QTLs reported on resistance to soybean cyst nematode No. of Population Parents QTLs Size Kefeng 1×Nannong 1138-2 3 201 Kefeng 1×Nannong 1138-2 1 184 Kefeng 1×Nannong 1138-2 6 201 Zheng 92116×Shang 3 105 951099 G.max‘7499’×G.soja PI 148 11 245331 Charleston×Dongnong 594 8 154 Jindou 2 23×Huibuzhiheidou Zhongdou 29×Zhongdou 28 255 32 3 Suinong 14×Suinong20 94 Total 65 Table 7. Information of 100-Seed Weight QTLs

Analysis methods CIM CIM IM

Population type RIL RIL RIL

IM

F2

CIM

BIL

CIM

RIL

CIM

RIL

CIM

RIL

CIM

F2

Reference Gai,2007 Zhang,2004 Wu,2001 Guan,2004 Li,2008 Chen,2007 Wang,2004 Wang,2008 Zhu, 2006

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Integration of Major QTLs of Important Agronomic Traits in Soybean

2.3.2 Lodging QTLs Soybean (Glycine max [L.] Merr.) grain yields may be reduced when the plants lodge, So soybean lodging characteristics are an important consideration when conditions favor high yields. Lodging also creates harvesting problems. Where higher yields are obtained under irrigation, lodging is a major factor to consider in variety selection, water management, and planting density. 59 QTLs from 16 papers and soybase website were listed in Table 8. For different research, 1 to 8 QTLs were obtained. Most research only got only 1 to 2 QTLs. The analytical methods included IM, CIM, ANOVA, SSMD, and MNTECRLO. The population type included RILs and F2, F5, and BC2F4. No. of QTLs 4 6 6 7 1 4 1 1 1 8 5 2 5 1 2 1 1 2 1 Total

Parents Kefeng 1×Nannong 1138-2 Zhongdou 29×Zhongdou32 Jindou 23 ×Huibuzhiheidou Pis×Beeson/ kenwood/ Lawrence G. max × G. soja Essex × Forrest Kefeng 1×Nannong 11382 Essex × Forrest G. max IA2008×G. soja PI468916 PI416937 × Young Minsoy × Noir 1 Archer × Minsoy Minsoy × Noir 1 --Noir 1 × Minsoy BSR 101 × LG82-8379 IA2008 × PI 468916 Coker237 × PI97100 --59

Population size

Analysis Methods

Population type

184

CIM

RIL

165

CIM

RIL

257

SSMD

RIL

236

CIM

RIL

120 100

CIM CIM

RIL RIL

206

CIM

RIL

100

CIM

RIL

468

CIM

BC2 F4

120 240 233 236 177 284 167 468 111 248

ANOVA IM IM ANOVA ANOVA ANOVA ANOVA MNTECRLO ANOVA BSA

F4 RIL RIL RIL F4:6 RIL F5 BC2F4 F2 RIL

Reference Huang,2008 Zhou,2009 Wang,2004 Guzman,2007 Li,2008 Li,2008 Zhang,2004 Kassem,2006 Wang,2003 Lee,1996 Orf,1999 Orf,1999 Chase,2001 Chase,2001 Mansur,1996 Kabelka, 2004 Wang,2003 Lee,1996 Mansur,1993

Table 8. Information of lodging QTLs in soybean 2.3.3 Plant height QTLs Plant height become one of important agronomic traits with the increase of planting density. Many factors affect the height of a soybean plant, but the genetic loci is the most important, and the rapid developments of molecular markers have provided powerful tools to map the height-related QTL at the genomic level. 93 QTLs from 13 papers and soybase website were

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Soybean - Molecular Aspects of Breeding

listed in Table 9. For different research, 1 to 19 QTLs were obtained. The analytical methods were mainly IM and CIM. The population type included RILs and F2. No. Of QTLs 5 4 19 5 8 1 6 1 11 2 8 8 15 Total

Population Analysis Population Reference size methods type Zheng92116×Shang951099 105 IM F2 Guan,2004 Kefeng1×Nannong1138-2 201 IM RIL Wu,2001 Zhongdou29×Zhongdou32 255 CIM RIL Wang,2008 Suinong14×Suinong20 94 CIM F2 Zhu,2006 Kefeng1×Nannong1138-2 184 CIM F2 Zhang,2004 G. max ‘7499’ × G. soja PI Li,2008 295 CIM RIL 245331 G. max IA2008×PI 468916 468 CIM RIL Wang,2004 Minsoy × Noir 1 IM RIL Mansur,1993 BSR 101× LG82-8379 167 RIL Kabelka,2004 Essex ×Williams RIL Chapman,2003 Jindou23 Wang Z,2004 CIM RIL ×Huibuzhiheidou Charleston×Dongnong594 154 CIM RIL Chen,2007 Charleston×Dongnong594 147 CIM & MIM RIL Sun,2010 93 Parents

Table 9. Information of soybean height QTLs 2.4 Soybean growth stages QTLs Soybean growth stages divides plant development into vegetative (V) and reproductive (R) stages. The vegetative stages are numbered according to how many fully-developed trifoliate leaves are present. The reproductive (R) stages begin at flowering and include pod development, seed development, and plant maturation. 98 QTLs from 10 papers and soybase website were listed in Table 10. For different research, 1 to 44 QTLs were obtained. The analytical methods were Multiple-QTL model, CIM, IECM, ANOVA, SIM, and Chisquare. The population type included F2, RIL and BC2. QTL-number 1 13 3 14 10 3 5 3 2 44 Total

Population type F2 F2 NIL RIL RIL RIL RIL BC2 F2 RIL 98

Analysis method Multiple-QTL model CIM IECM ANOVA SIM ANOVA Chi-square CIM ANOVA CIM

Table 10. QTLs published of soybean growth stages

References Naoki,2001 Wang Zhen,2004 Wang Ying,2008 Green,2001 Adler,1999 Funastuki,2005 Chase,2001 Diers,2004 Mansur,1996 Dawei Xin,2007

Integration of Major QTLs of Important Agronomic Traits in Soybean

91

3. QTL projection and integration: a consensus map of soybean important agronomic traits 3.1 QTL projection of seed quality traits 3.1.1 Oil content QTLs projection on a consensus map In total, 110 QTLs for oil content were projected onto the reference map soymap2, and a consensus map was generated. The projected QTLs covered all LGs of the reference map (Fig. 1). Some QTLs could not be projected onto the reference map because they had no common markers with the reference map. As Fig. 1 shows, each LG contained many QTL clusters, and at least two original QTLs were found in each QTL cluster. On LG A1, 7 independent QTLs from different research were projected on the same interval.

Fig. 1. Consensus map of soybean oil content

92

Soybean - Molecular Aspects of Breeding

3.1.2 Protein concentration QTLs projection on a consensus map In total, 70 QTLs of 23 clusters identified, Fig. 2 illustrates the QTL clusters identified: there appear to be one very common site on chromosomes I, at this site, nine QTL was predicted from the various populations to be present. Four metaQTL sites, one each on chromosomes A1, B2, E, and M, at each of these sites, at least four QTL were predicted from the various populations to be present. Eighteen metaQTL site, one each on chromosomes A2, B1, B2, C1, C2, E, G, H, N and O. At each of these sites, at least two QTL were predicted from the various populations; the CIs of these clustered QTL shared a 3cM overlap with one another.

Fig. 2. Consensus map of soybean protein concentration

Integration of Major QTLs of Important Agronomic Traits in Soybean

93

3.1.3 Fatty acid content QTLs projection on a consensus map Different genetic map with their QTLs were projected on the soybean public map Soymap2 to construct a consensus map of major QTLs for fatty acid content in soybean. The consensus QTLs distributed in clusters on A1, B2, D1b, D2, E, G, and L linkage groups (Fig.3).

Fig. 3. Consensus map of fatty acid QTLs in soybean 3.1.4 Amino acid content QTLs projection on a consensus map For amino acid content in soybean, 26 consensus QTLs for different amino acid on 16 linkage groups, that is A1, A2, B2, C1, D1a, D1b, D2, E, F, G, I, J, K, L, M, and O, were constructed (Fig.4). However, each consensus QTL included not only one kind but a few different kind of amino acids, which would be a key to explain that the same locus could influence the content of many kind of amino acids.

94

Soybean - Molecular Aspects of Breeding

Fig. 4. Consensus map of Amino acid content QTLs in soybean 3.1.5 Isoflavone content QTLs projection on a consensus map For isoflavone content in soybean, 10 consensus QTLs for different amino acid on 6 linkage groups, A1, B1, B2, H, K, and N, were constructed.

Integration of Major QTLs of Important Agronomic Traits in Soybean

95

Fig. 5. Isoflavone content QTLs projection on a consensus map 3.2 QTL projection of biotic stress resistance traits 3.2.1 Fungal disease resistance QTLs projection on a consensus map QTLs of original map were projected on the reference map by map-projection function of BioMercator2.1. In total, 107 QTLs of fungal disease resistance were projected on the reference map, soymap2, and a consensus map was obtained (Fig. 6). As the Fig. 6 shown, projected QTLs were covered 11 linkage groups of the reference map. Although these original QTLs were mapped in different genetic backgrounds and with different methods, they were projected on the same regions by the common marker. If a QTL cluster contained more than two QTLs from different researches, this region could contain a potential allele in a high probability. 3.2.2 Insect-resistance QTLs projection on a consensus map 81 QTLs of original map were projected on the reference map. For single insect-resistance trait, the consensus QTLs mainly focused on 4 linkage groups, E, F, H, and M (Fig. 7 ). The cluster on linkage group E was obviously in a narrow interval. In detail, 3 consensus QTLs were distributed on linkage group F and M for soybean aphid-resistance. 9 consensus QTLs were discovered on 6 linkage group D1a, D2, E, G, H, and M for Corn Earworm. No consensus QTLs were found for Common Cutworm. For multiple insect-resistance trait, 14 concensus QTLs were combined on 8 linkage group D1a, D2, E, F, G, H, M, and N(Fig. not shown).

96

Soybean - Molecular Aspects of Breeding

Fig. 6. QTLs of original map projection. BSR (Brown stem rot) ; Phytoph (Phytophthora root rot) ; ASR (Asian soybean rust) ; Sclero (Sclerotinia stem rot, White mold) ; SBR (Soybean rust) ; SDS (Sudden death syndrome) ; FLS (Frogeye leaf spot) ; RR-HR (Rhizoctonia root and Hypocotyl rot) ; PSD (Phomopsis seed decay)

Integration of Major QTLs of Important Agronomic Traits in Soybean

97

Fig. 7. Consensus map of insect-resistance QTLs in soybean 3.2.3 Soybean cyst nematode resistance QTLs projection on a consensus map 151 QTLs of original map were projected on the reference map(Fig. 8 ). 16 consensus QTLs on 8 linkage groups A2, B1, B2, D2, E, G, H, and J were integrated. In detail, 3 consensus QTLs were distributed on linkage group B1, B2, and G for SCN Race 1-resistance. 1 consensus QTLs were distributed on linkage group B1 for SCN Race 2-resistance. 7 consensus QTLs were distributed on linkage group A2, E, G, and J for SCN Race 3resistance. 3 consensus QTLs were distributed on linkage group A2, G, and H for SCN Race 4-resistance. 1 consensus QTLs were distributed on linkage group B1 for SCN Race 5resistance. 1 consensus QTLs were distributed on linkage group D2 for SCN Race 14resistance. 3.3 QTL projection of yield traits 3.3.1 100 seed weight QTLs projection on a consensus map 65 QTLs of original map were projected on the reference map (Fig. 9 ). 10 consensus QTLs on 9 linkage groups B1, C2, D2, K, M, and O were integrated for additive effect. 4 consensus QTLs on 3 linkage groups B2, H, and I were clusted for reductive effect. 3.3.2 Lodging QTLs projection on a consensus map 59 QTLs of original map were projected on the reference map(Fig. 10 ). 11 consensus QTLs were distributed on 5 linkage group, B1, C2, F, G, and L. Only one consensus cluster were found on linkage group B1 and G, but 2 on linkage group C2 and F, and 5 on linkage group L.

98

Soybean - Molecular Aspects of Breeding

Race1

Race3

Race1

Race4

Race2

Race1

Race4

Fig. 8. Consensus map of QTLs resistant to SCN

Race3

Race4

Race3

Race5

Race3

Race14

Integration of Major QTLs of Important Agronomic Traits in Soybean

99

Fig. 9. Consensus map of 100 seed weight in soybean

Fig. 10. Consensus map of Lodging QTLs 3.3.3 Plant height QTLs projection on a consensus map 78 QTLs of original map were projected on the reference map(Fig. 11 ). 12 consensus QTLs were distributed on 7 linkage group, B1, C2, D1a, F, G, K, and M.

100

Soybean - Molecular Aspects of Breeding

Fig. 11. Height QTLs projection on a consensus map 3.4 Soybean growth stage QTLs projection on a consensus map 98 QTLs of original map were projected on the reference map (Fig. 12). 7 consensus QTLs were distributed on 3 linkage group C2, L, and M for R1 period. 2 consensus QTLs were distributed on 2 linkage group C2 and L for R8 period. 10 consensus QTLs were distributed on 5 linkage group C1, D1a, D1b, F, and J for mixed periods(Fig. not all shown).

Fig. 12. Consensus map of soybean growth stage

101

Integration of Major QTLs of Important Agronomic Traits in Soybean

4. Meta-analysis for important agronomic traits 4.1 Meta-analysis results of seed quality QTLs 4.1.1 Meta-analysis of Oil content QTLs A meta-analysis was carried out on the consensus QTL sites on 25 QTL clusters (Table 11 ). The site on linkage group A1 merged five QTL, with flanking markers at 92.56cM and 94.20cM, a C.I. of 1.49cM and an R2 value of 10.83%. The site on linkage group I merged five QTL into a single consensus QTL. The flanking markers for this site lay at 36.03cM and 49.30cM, the CI of the QTL was 12.35cM, and its R2 value was 18.22%. The site on linkage group G merged four QTL into a single consensus QTL. The flanking markers for this site lay at 94.40cM and 96.60cM, the CI of the QTL was 1.71cM, and its R2 value was 13.27%. The confidence interval at all sites ranged from 1.49-12.35cM. The mean R2 values ranged from 5.5% to 39.15%.

LG

Meta95% C.I. AIC QTL (cM) value position( cM)

A1

48.99

93.5

92.75-94.24

1.49

5

10.83

A170_1

92.56

Satt511

94.20

A1

48.99

29.19

28.18-30.21

2.03

2

29.18

Satt454

28.08

A329_2

30.28

A1

48.99

88.24

86.91-89.57

2.66

2

38.41

Satt599

85.58

Bng077_1

91.24

A2

18.01

70.96

66.97-74.95

7.98

2

39.15

A111_1

67.328

Satt341

77.695

B2

53.37

72.83

69.15-76.51

7.36

3

9.56

Sat_083

68.98

Sat_009

78.66

C1

54.37

10.43

7.68-13.19

5.51

3

8.67

Satt690

5.36

A351_2

15.70

1.98

2

13.6

AI794821

122.63

Satt338

123.79

C1

54.37

123.94

122.95124.93

Map distance

ROriginal L-marker Mean marker QTL L-marker Position R-marker R2 Position number (cM) (cM)

C2

36.90

98.42

96.2-100.63

4.43

2

5.50

R092_3

96.13

Sat_076

99.18

D1a

28.99

57.45

56.53-58.36

1.83

3

8.56

Satt254

56.40

Satt203

59.00

D1a

28.99

70.74

68.87-72.62

3.75

2

9.10

Satt198

68.62

Satt439

72.30

D2

22.25

77.41

75.24-79.58

4.34

3

10.17

Sat_292

75.29

Satt461

80.20

E

22.24

26.14

23.51-28.77

5.26

2

14.35 OP_M12b

22.84

K229_1

28.30

E

22.24

32.71

29.92-35.5

5.58

2

20.50

28.27

Satt573

35.80

F

27.42

2.16

1.29-3.02

1.73

2

12.17 M8E6mr1

1.09

Satt343

3.04

F

27.42

18.45

16.11-20.80

4.69

2

13.59

Satt252

16.08

Satt423

20.56 73.40

K229_1

G

33.03

67.32

66.09-71.10

5.01

3

12.53

A073_1

68.56

Sat_143

G

33.03

95.12

94.26-95.97

1.71

4

13.27

Sct_199

94.40

Satt191

96.60

H

16.58

89.27

88.33-90.22

1.89

3

9.63

Satt142

86.49

A748_2

90.30

I

27.16

32.5

30.93-34.07

3.14

2

27.00

B214_2

30.56

A352_2

34.70

I

27.16

37.4

36.23-48.58

12.35

5

18.22

Sat_219

36.03

Sat_105

49.30

K

29.90

102

98.26105.74

7.48

2

11.20

R

97.13

M7E8mr3

107.00

L

49.37

34.78

33.84-35.71

1.87

2

19.50

Satt497

33.71

Satt613

36.10

L

49.37

93.10

91.60-94.60

3.00

3

11.83 DUBC015

90.34

A489_1

95.40

N

17.54

73.14

69.19-77.09

7.90

2

6.57

A808_1

68.76

Sat_304

77.10

N

15.19

95.56

92.22-98.90

6.68

3

37.31

Satt257

92.56

Satt022

102.06

Table 11. Meta-analysis results of oil content QTLs

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Soybean - Molecular Aspects of Breeding

4.1.2 Meta-analysis of protein concentration QTLs A meta-analysis was carried out on the consensus QTL sites on 23 QTL clusters (Table 12). The site on chromosome I merged nine QTL from seven populations, with flanking markers at 33.3cM and 37.0cM, a CI of 3.7cM and an R2 value of 20.8%. The site on chromosome A1 merged four QTL, predicted from four populations, into a single consensus QTL. The flanking markers for this site lay at 88.83cM and 97.49cM, the CI of the QTL was 8.66cM, and its R2 value was 9.4%. The site on chromosome B2 merged four QTL, predicted from two populations, into a single consensus QTL. The flanking markers for this site lay at 71.7cM and 73.22cM, the CI of the QTL was 1.52cM, and its R2 value was 3.5%. The site on chromosome E merged five QTL, predicted from three populations, into a single consensus QTL. The flanking markers for this site lay at 23.5cM and 30.3cM, the CI of the QTL was 6.7cM, and its R2 value was 7.4%. The site on chromosome M merged five QTL (five populations), with flanking markers at 34.2cM and 41.5cM, a CI of 7.2cM and an R2 value of 12.4%. In total, twenty-three consensus QTL were detected. The confidence interval at all sites ranged from 1.52-14.31cM, and the proportion of the phenotypic variance associated with each of them from 1.5%-20.8%.

LG

AIC value

Meta-QTL Position (cM)

A1 15.64

93.16

A2 39.23

147.79

95% C.I. (cM) 88.83-97.49

Origina Mean Map l R2 distance QTL number

L-marker

LR-marker marker R-marker Position Position (cM) (cM)

8.66

4

9.40

Satt174

88.58

Sat_271

97.76

142.48-153.09 10.61

2

3.10

T036_1

141.08

Satt228

154.11 36.48

B1 16.19

32.56

29.33-35.79

6.46

3

7.00

A109_1

29.17

Satt251

B2 7.98

31.16

25.86-36.46

10.60

2

14.50

Sat_342

20.31

A343_1

37.99

B2 7.98

49.65

44.35-54.95

10.60

2

6.80

B142_1

43.58

Satt168

55.20

Satt272

71.68

DOP_F04

73.54

10.34

A463_1

21.04

B2 7.98

72.46

71.70-73.22

1.52

4

3.50

C1 73.46

15.69

10.39-20.99

10.60

2

9.40 SOYGPATR

C1 73.46

63.08

57.78-68.38

10.60

2

13.70

V38a

54.19

A519_3

69.30

C1 73.46

93.85

88.55-99.15

10.60

2

11.00

Sat_207

87.31

Bng044_2

107.61

C1 73.46

123.76

119.43-128.09

8.66

3

13.20

Bng012_1

108.08

Satt164

132.46

C2 29.4

119.19

115.55-122.82

7.27

3

15.40

Satt708

115.49

A538_1

123.37

E 54.51

26.90

23.55-30.25

6.70

5

7.40

OP_M12b

22.84

B174_1

30.88

E 54.51

45.21

43.88-45.98

2.10

3

12.00

Satt268

44.27

R028_2

46.10

E 18.24

61.40

56.08-66.72

10.64

3

5.40

A226H_2

54.85

Satt553

67.92

G 43.52

67.62

62.31-72.92

10.61

2

13.80

Satt199

62.16

Sat_143

73.42

G 43.52

93.60

88.30-98.91

10.61

2

10.20

AF162283

87.94

L154_1

99.33

H 26.99

87.97

81.09-93.70

12.61

3

9.20

Satt302

81.04

A810_1

97.53

I 77.57

35.78

33.31-36.97

3.66

9

20.80

A144_1

32.42

Satt239

36.94

33.47

DOP_H1 4

41.84

M 42.72

37.89

34.27-41.50

7.23

5

12.38

Satt567

N 18.33

32.38

27.07-37.68

10.61

2

9.50

Satt631

26.14

Satt584

37.98

N 9.78

79.50

74.50-84.50

10.00

3

11.00

BLT015_1

74.49

Satt234

84.60

N 9.78

98.03

94.02-102.06

8.04

2

1.50

Sat_306

93.11

Satt022

102.06

O 14.12

72.10

64.95-79.26

14.31

2

7.50

Sat_282

63.81

Satt477

82.09

Table 12. Meta-analysis results of protein concentration QTL

103

Integration of Major QTLs of Important Agronomic Traits in Soybean

4.1.3 Meta-analysis of Fatty acid QTLs A meta-analysis was carried out on the consensus QTL sites on 19 QTL clusters (Table 13 ). Some sites were related with only one trait. The site on linkage group A1, with flanking markers Sat_137 and A487_1, a C.I. of 7.44cM was related with Palmitic Acid Content. The site on linkage group B2, with flanking markers Sat_189 and Satt556, a C.I. of 0.4cM was related with Stearic Acid Content. The site on linkage group D2, with flanking markers Satt461 and Sat_114, a C.I. of 8.66 cM was related with Olelic Acid Content. The site on linkage group N, with flanking markers Satt530 and Satt683, a C.I. of 3.34cM was related with Linoleic Acid Content. The site on linkage group E, with flanking markers OP_M12b and A636_1, a C.I. of 3.09cM was related with Linolenic Acid Content. However, several sites were related with at least two traits. The site on linkage group C2, with flanking markers Sat_312 and Satt319, a C.I. of 10.6cM was related with Olelic Acid Content and Palmitic Acid Content. The site on linkage group D2, with flanking markers L026_1 and Satt615, a C.I. of 0.38cM was related with Linoleic Acid Content and Olelic Acid Content. The site on linkage group G, with flanking markers K493_1 and T005_2, a C.I. of 6.12cM was related with Olelic Acid Content, Pamitic Acid Content, and Stearic Acid Content. Trait

LG

AIC value

Meta-QTL position(cM)

95% C.I. (cM)

Map distance

L-marker

R-marker A487_1

Pal

A1

64.01

3.54

0-7.44

7.44

Sat_137

Pal

A1

64.01

15.76

10.46-21.07

10.61

Satt572

Satt276

Ole

A1

64.01

92.41

89.06-95.77

6.71

A104_1

A170_1

St

B2

60.00

73

72.8-73.2

0.4

Sat_189

Satt556

Lio

B2

60.00

90.49

86.19-94.78

8.59

AW620774

A741_1

Ole,Pal

C2

11.04

113.39

108.09118.69

10.6

Sat_312

Satt319

Pal

D1b

28.81

73.12

70.57-75.67

5.10

Satt141

Satt290

Ole

D2

34.21

83.00

78.67-87.33

8.66

Satt461

Sat_114

Lin,Ole

D2

34.21

91.00

90.81-91.19

0.38

L026_1

Satt615

Lio

E

35.08

25.38

23.84-26.93

3.09

OP_M12b

A636_1

Lio,Ole

E

35.08

45.09

41.34-48.84

7.50

Satt483

Satt452

Ole,Lin

G

127.82

0.72

0-3.65

3.65

Satt163

Satt038

Ole,lin

G

127.82

21.90

16.59-27.2

10.61

Satt235

Satt130

Ole

G

127.82

49.25

45.5-53

7.50

Satt131

Satt566

Ole,Pal,St

G

127.82

80.67

77.61-83.73

6.12

K493_1

T005_2

Lin,Lio

K

11.08

42.89

37.58-48.19

10.61

Satt555

Sct_196

Ole

L

69.71

31.89

27.56-36.22

8.66

Sat_397

Sat_191

Ole

L

69.71

66.11

62.36-69.86

7.50

gy3E_1

Satt166

Lin

N

6.69

34.09

32.42-35.76

3.34

Satt530

Satt683

Note: Pal, St, Ole, Lin, Lio were the abbreviation of Palmitic acid, Stearic acid, Oleic acid, Linoleic acid, and Linolenic acid, individually.

Table 13. Meta-analysis results of of fatty acid QTLs

104

Soybean - Molecular Aspects of Breeding

4.1.4 Meta-analysis of amino acid content QTLs A meta-analysis was carried out on the consensus QTL sites on 26 QTL clusters (Table 14). Most sites were related with more than two kind of amino acid content. The site on linkage group A1, with flanking markers Sat_344 and Sat_410 at 19.38cM and 29.63cM, a C.I. of 8.66cM was an admixture related with Glycine, Threonine, and Alanine content. The site with the minimal confidence interval on linkage group L, with flanking markers Satt495 and Sat_408 at 0.00 cM and 1.31cM, a C.I. of 1.86cM was related with Alanine, Glycine, Serine, Threonine, and Phenylalanine content. However, the site on linkage group M, with flanking markers GMSC514 and Satt201 at 3.05cM and 13.56cM, a C.I. of 8.66cM was mainly related with Methionine content.

LG

AIC value

Meta-QTL position(cM)

95% C.I. (cM)

Map distanc e

L-marker

L-marker R-marker Position R-marker Position (cM) (cM)

A1

23.69

25.56

21.23-29.89

8.66

Sat_344

19.38

Sat_410

29.63

A2

47.19

36.77

32.44-41.10

8.66

Satt589

33.96

Satt315

45.29

A2

47.19

107.05

102.72-111.38

8.66

Satt233

100.09

Sct_194

113.57

B2

24.61

55.2

51.45-58.95

7.50

Sct_034

51.45

Satt416

56.96

C1

11.04

74.46

69.16-79.76

10.60

Satt607

67.03

Satt476

80.62

D1a

47.19

59

55.65-62.35

6.70

Satt515

55.68

Satt580

62.37

D1b

53.30

75.67

70.37-80.97

10.60

Sat_135

70.65

Satt644

79.42

D1b

53.30

116.35

113.52-119.18

5.66

Sat_183

112.63

Sat_198

118.95

D2

47.37

47.73

42.43-53.03

10.6

Satt372

39.35

Satt582

53.85

D2

47.37

92.12

89.06-95.18

6.12

Satt488

89.20

Satt301

93.71

E

11.04

44.76

39.46-50.06

10.60

Sat_136

39.16

Sat_273

47.50

F

38.17

16.08

13.43-18.73

5.30

Satt269

11.37

Satt149

18.13

G

62.32

16.4

13.05-19.75

6.7

Satt570

12.74

Satt235

21.89

G

62.32

54.51

50.76-58.26

7.50

Satt303

53.42

Sat_088

58.00

I

11.04

82.78

77.48-88.08

10.60

Satt330

77.84

Satt623

92.52

J

23.69

11.74

6.44-17.04

10.60

AW310961

5.19

Satt674

15.95

J

23.69

43.01

37.71-48.31

10.60

Sat_370

37.40

Sat_366

52.84

K

40.45

30.28

25.95-34.61

8.66

Satt102

30.28

Satt137

36.99

K

40.45

46.63

41.33-51.93

10.60

Satt178

40.86

Sat_116

52.28

L

103.86

0.00

0.00-1.86

1.86

Satt495

0.00

Sat_408

1.31

L

103.86

31.72

29.22-34.22

5.00

Sat_405

29.62

Satt313

34.54

L

103.86

61.35

56.05-66.65

10.6

Satt156

56.14

Satt166

66.51

M

101.13

7.84

3.51-12.17

8.66

GMSC514

3.05

Satt201

13.56

M

101.13

33.47

29.14-37.80

8.66

Satt567

33.47

Satt435

38.94

M

101.13

73.37

70.87-75.87

5.00

Satt494

71.71

Sat_288

76.41

O

35.53

13.60

10.25-16.95

6.70

Satt487

9.53

Satt492

17.25

Table 14. Meta-analysis results of amino acid content QTLs

105

Integration of Major QTLs of Important Agronomic Traits in Soybean

4.1.5 Meta-analysis of isoflavone content QTLs A meta-analysis was carried out on the consensus QTL sites on 10 QTL clusters (Table 15 ). Although there were five sub-component of isoflavone content. Most sites were related with more than only one kind among them. The site on linkage group A1, with flanking markers Sat_368 and Satt165 at 10.60cM and 14.37cM, a C.I. of 10.60cM was related with daidyzin content. The three sites on linkage group B1, with flanking left markers Sat_261, Satt 197, and Sct_026 at 32.95 cM, 46.39, and 78.13cM, corresponding right markers Satt638, Sat_247, and Satt444 at 37.80cM, 49.73cM, and 85.92cM, the C.I. of 6.12cM, 6.12cM, and 7.55cM were all related with Gly content.

LG

AIC Meta-QTL value position(cM)

A1

95% C.I. (cM)

Map L-marker R-marker distanc L-marker Position R-marker Position e (cM) (cM)

11.04

17.16

11.86-22.46

10.60

Sat_368

14.37

Satt165

23.00

B1 109.03

36.48

33.42-39.54

6.12

Sat_261

32.95

Satt638

37.80

B1 109.03

46.39

43.33-49.45

6.12

Satt197

46.39

Sat_247

49.73

B1 109.03

81.7

77.90-85.45

7.55

Sct_026

78.13

Satt444

85.92

B2

11.04

93.49

88.19-98.79

10.60

Satt534

87.59

Satt560

97.92

H

15.57

81.04

76.71-85.37

8.66

Sat_158

73.46

Satt637

85.79

K

40.51

41.52

36.21-46.82

10.61

Satt055

32.96

Satt727

46.80

K

40.51

52.28

47.95-56.61

8.66

Satt337

47.38

Satt273

56.62

N

29.61

45.14

39.84-50.44

10.60

Sat_208

39.35

Satt387

53.25

N

29.61

74.99

70.66-79.32

8.66

Satt549

53.25

Sat_091

79.51

Table 15. Meta-analysis results of isoflavone content QTLs 4.2 Meta-analysis results of biotic stress resistance QTLs 4.2.1 Meta-analysis of fungal disease resistance QTLs A meta-analysis was carried out on the consensus QTL sites on 23 QTL clusters (Table 16 ). In total, 9 kind of soybean fungal disease-resistance QTLs were integrated. For brown stem rot, in short BSR, one site was the most notable cluster of integrating 14 former researches with flanking markers Sctt011 and Satt547 at 62.88cM and 67.79cM on linkage group J, a C.I. of 3.78cM. For phytophthora root rot, in short phytoph, one site was on linkage group F with flanking markers Satt252 and Satt149 at 16.08cM and 18.12cM, a C.I. of 0.84cM. Another was on linkage group J with flanking markers Satt414 and Satt596 at 37.04cM and 39.64cM, a C.I. of 1.82cM. For sclerotinia stem rot, in short Sclero, the former research results were more dispersed. one main site was on linkage group E with flanking markers Satt720 and A517_1 at 20.80cM and 26.02cM, a C.I. of 4.52cM. Another was on linkage group O with flanking markers Satt243 and Sat_190 at 119.50cM and 129.80cM, a C.I. of 8.66cM. For sudden death syndrome, in short SDS, one main site was on linkage group D2 with flanking markers Satt662 and Sat_001 at 87.88cM and 92.12cM, a C.I. of 4.11cM. Another was on linkage group G with flanking markers Satt163 and B053_1 at 0.00cM and 5.77cM, a C.I. of 4.21cM. Other results for remain diseases were listed in table and above Fig. 6.

106

Soybean - Molecular Aspects of Breeding

LG

AIC Meta-QTL 95% C.I. value position(cM) (cM)

A2

24.04

36.77

A2

24.04

106.72

C2

26.31

40.30

C2

26.31

115.47

D2 D2 E F F F G G G G J J J K L M N O

53.21 53.21 13.94 32.21 32.21 32.21 117.8 117.8 117.8 117.8 139.2 139.2 139.2 13.94 19.65 25.49 15.83 43.87

81.29 90.04 23.85 16.91 19.21 84.17 3.87 46.80 83.00 95.70 38.36 49.98 65.66 80.43 30.93 54.83 42.15 84.99

O

43.87

125.43

31.47-42.07 102.46110.98 35.00-45.60 111.23119.70 77.54-85.04 87.98-92.09 21.59-26.11 16.49-17.33 17.99-20.43 78.87-89.47 1.77-5.98 43.31-50.29 78.20-87.79 90.40-101.01 37.45-39.27 46.98-52.98 63.77-67.55 73.15-87.71 26.02-35.85 51.08-58.58 41.40-42.91 79.77-90.21 121.10129.76

Map Ldistance marker

L-marker R-marker Position R-marker Position (cM) (cM) 28.44 Satt315 45.29

10.60

Satt480

8.52

Satt233

100.09

Satt329

110.94

10.60

Sat_062

30.80

Satt291

45.76

8.47

Satt365

111.68

Staga001

119.85

7.50 4.11 4.52 0.84 2.44 10.6 4.21 6.98 9.59 10.61 1.82 6.00 3.78 14.56 9.83 7.50 1.51 10.44

Sat_222 Satt662 Satt720 Satt252 Satt252 Satt334 Satt163 Sat_308 A121_2 A245_2 Satt414 Sat_093 Sctt011 Satt499 Satt388 Satt463 Sat_275 A878_1

76.69 87.88 20.80 16.08 16.08 78.06 0.00 43.09 78.05 89.97 37.04 46.09 62.88 71.00 23.55 50.10 40.81 78.33

Satt226 Sat_001 A517_1 Satt149 Satt423 Sat_313 B053_1 Satt352 AF162283 Sat_117 Satt596 Sat_366 Satt547 A841_1 Satt613 Satt626 Sat_280 Bng070_1

85.15 92.12 26.02 18.12 20.56 91.87 5.77 50.53 87.94 100.00 39.64 52.84 67.79 87.04 36.05 58.60 43.45 89.98

8.66

Satt243

119.50

Sat_190

129.80

Table 16. Meta-analysis results of fungal disease resistance QTLs 4.2.2 Meta-analysis of insect-resistance QTLs A meta-analysis was carried out on the consensus QTL sites on 14 QTL clusters (Table 17 ). The QTL intervals were reduced from 15 cM to 3.67 cM in average. For single insectresistance, only the sites of soybean aphid-resistance and corn earworm-resistance got the meta-analysis results. 3 true QTLs were related with soybean aphid resistance. Two sites were on linkage group F, with flanking left markers j11_1 and R045_1 at 7.31 cM and 70.12cM, corresponding right markers BLT030_1 and Satt510 at 8.67cM and 71.41cM, the C.I. of 4.68cM and 2.37cM. The other was on linkage group M with flanking markers DOP_H14 and A131_1 at 41.84cM and 47.12cM, a C.I. of 4.07cM. 9 true QTLs were related with corn earworm resistance. One site was on linkage group D1a with flanking markers Sat_353 and R013_2 at 36.23cM and 38.09cM, a C.I. of 6.02cM. Another was on linkage group G with flanking markers Satt472 and Satt191 at 94.84cM and 96.57cM, a C.I. of 1.22cM. Others were shown in the Table 17.

107

Integration of Major QTLs of Important Agronomic Traits in Soybean

Most QTLs were related with mutiple insect-resistance(Table 18). For example, The site on linkage group E, with flanking markers A135_3 and Satt575 at 0.06cM and 3.30cM, a C.I. of 3.11cM was related with corn earworm resistance and common cutworm. Meta-QTL AIC position(c value M)

95% C.I. (cM)

RLmarker marker R-marker L-marker Position Position (cM) (cM)

Map distance

Trait

LG

Soybean aphid

F

30.07

7.56

5.22-9.90

4.68

j11_1

7.31

BLT030_1

8.67

Soybean aphid

F

30.07

71.09

69.90-72.27

2.37

R045_1

70.12

Satt510

71.41

Soybean aphid M

34.20

43.85

41.82-45.89

4.07

DOP_H14

41.84

A131_1

47.12

15.8

38.08

35.07-41.09

6.02

Sat_353

36.23

R013_2

38.09

CEW

D1a

CEW

D2

10.64

100.12

95.33-104.92

9.59

CEW

E

48.13

2.14

0.59-3.70

3.11

GMHSP179 99.04

Satt186

105.45

A135_3

0.06

Satt575

3.30 96.57

CEW

G

8.77

95.71

95.10-96.32

1.22

Satt472

94.84

Satt191

CEW

H

145.95

38.53

35.69-41.37

5.68

A036_1

34.29

Sctt009

38.89

CEW

H

145.95

57.34

54.52-60.16

5.64

A130_1

56.18

Satt469

58.91

CEW

H

145.95

77.32

75.9-78.74

2.84

Sat_158

73.46

K327_1

77.53

CEW

M

31.68

56.69

55.28-58.11

2.83

Satt220

56.29

A584_3

58.50

CEW

M

31.68

60.75

60.46-61.03

0.57

Sat_258

60.47

Satt702

61.04

Table 17. Meta-analysis results of single insect-resistance QTLs

LG

AIC Meta-QTL value position(cM)

95% C.I. (cM)

Map distance L-marker

L-marker R-marker Position R-marker Position (cM) (cM)

D1a

25.01

38.08

35.07-41.09

6.02

Sat_353

36.23

R013_2

38.09

D2

10.64

100.12

95.33-104.92

9.59

GMHSP179

99.04

Satt186

105.45

E

48.13

2.14

0.59-3.70

3.11

A135_3

0.06

Satt575

3.30

F

50.58

7.56

5.22-9.90

4.68

j11_1

7.31

BLT030_1

8.67

F

50.58

71.03

69.84-72.22

2.38

R045_1

70.12

Satt510

71.41 96.57

G

22.76

95.71

95.27-96.14

0.87

Satt472

94.84

Satt191

H

235.12

35.04

32.39-37.69

5.30

A036_1

34.29

Sctt009

38.89

H

235.12

57.56

55.65-59.48

3.83

A130_1

56.18

Satt469

58.91

H

235.12

68.96

68.07-69.84

1.77

Satt676

68.86

Satt314

69.12

H

235.12

81.57

79.75-83.40

3.65

Satt302

81.04

Sat_175

83.19

M

124.22

42.75

40.85-44.64

3.79

DOP_H14

41.84

A131_1

47.12

M

124.22

56.70

55.74-57.66

1.92

Satt220

56.29

A584_3

58.50

M

124.22

60.74

60.54-60.94

0.4

Sat_258

60.47

Satt702

61.04

N

7.28

25.60

23.53-27.66

4.13

BLT004_1

25.49

Satt631

26.14

Table 18. Meta-analysis results of multiple insect-resistance QTLs

108

Soybean - Molecular Aspects of Breeding

4.2.3 Meta-analysis of soybean cyst nematode resistance QTLs A meta-analysis was carried out on the consensus QTL sites on 16 QTL clusters (Table 19 ). 3 true QTLs were related with SCN race1 resistance. The main site was on linkage group G, with flanking markers H3_c6_2 and Satt309 at 2.77 cM and 4.53cM, the C.I. of 1.48cM. One true QTL was simultaneously related with SCN race2 resistance and SCN race5 resistance on linkage group B1 with flanking markers R244_1 and AQ851479 at 115.75cM and 128.66cM, a C.I. of 12.29cM. 7 true QTLs were related with SCN race3 resistance. The main site was on linkage group G with flanking markers Satt309 and B053_1 at 4.53cM and 5.77cM, a C.I. of 1.01cM. 3 true QTLs were related with SCN race4 resistance. The main site was on linkage group A2, with flanking markers Sat_162 and A486_1 at 51.86 cM and 53.16cM, the C.I. of 0.86cM. One true QTL was related with SCN race14 resistance on linkage group D2 with flanking markers Satt528 and i6_2 at 86.34cM and 93.92cM, a C.I. of 7.50cM. 95% C.I. (cM)

L-marker Map L-marker Position distance (cM)

R-marker

R-marker Position (cM)

85.92

Sat_123

100.88

53.54

A329_1

62.74

H3_c6_2

2.77

Satt309

4.53

12.29

R244_1

115.75

AQ851479

128.66

5.70

Satt187

54.92

A975_2

61.29 39.98

Trait LG

AIC Meta-QTL value position(cM)

Race1 B1

11.72

94.66

89.08-100.24

11.16

Satt444

Race1 B2

27.24

57.89

54.52-61.26

6.74

A018_1

Race1 G

51.3

3.79

3.05-4.53

1.48

Race2 B1

19.81

121.68

115.53-127.82

Race3 A2 53.68

58.43

55.58-61.28

Race3 E

103.64

38.31

36.47-40.15

3.68

Satt573

35.79

A386_1

Race3 G 334.49

5.11

4.61-5.62

1.01

Satt309

4.53

B053_1

5.77

Race3 G 334.49

30.91

27.88-33.94

6.06

Sat_315

27.48

Sat_403

34.87

Race3 G 334.49

72.98

70.13-75.84

5.71

Satt517

69.87

Satt288

76.77

Race3 G 334.49

94.8

91.45-98.15

6.70

A245_2

89.97

H3_54HE_1

98.52

Race3 J

20.29

74.00

69.67-78.33

8.66

Sat_396

69.30

Satt431

78.57

Race4 A2

9.90

52.30

51.87-52.73

0.86

Sat_162

51.86

A486_1

53.16

Race4 G

12.36

3.87

3.20-4.53

1.33

H3_c6_2

2.77

Satt309

4.53

Race4 H

18.30

59.33

54.13-64.53

10.40

Satt541

53.35

Satt052

64.10

Race5 B1

27.13

121.68

115.53-127.82

12.29

R244_1

115.75

AQ851479

128.66

Race14 D2 22.17

90.20

86.45-93.95

7.50

Satt528

86.34

i6_2

93.92

Table 19. Meta-analysis results of resistance QTLs to soybean cyst nematode 4.3 Meta-analysis results of yield QTLs 4.3.1 Meta-analysis of 100-seed weight QTLs A meta-analysis was carried out on the consensus QTL sites on 10 QTL clusters (Table 20 ). All the sites were dispersed on 9 linkage groups, only 2 clusters were found on LG B2. The site with the minimal confidence interval was on linkage group D2, with flanking markers Satt458 and Satt135 at 24.52 cM and 26.05cM, the C.I. of 1.52cM. 4.3.2 Meta-analysis of lodging QTLs A meta-analysis was carried out on the consensus QTL sites on 11 QTL clusters (Table 21 ). 5 clusters were found on LG L with the interval of 1.31-6.19cM, 7.85-14.03cM, 30.58-34.14cM,

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36.70-41.00cM, and 92.66-111.07cM, the C.I. were 3.55cM, 3.82cM, 3.12cM, 2.78cM, and 17.04cM, individually. The site with the minimal confidence interval was on LG G, with flanking markers Sat_372 and L120_1 at 107.75 cM and 110.45cM, the C.I. was 1.73cM.

R-marker

R-marker Position (cM)

36.48

Satt197

46.39

43.58

A108_1

53.54

BLT049_2

59.82

G214_4

70.88 112.19

LG

AIC value

Meta-QTL position(cM)

95% C.I. (cM)

Map L-marker distance

B1

27.34

40.85

36.88-44.83

7.95

Satt251

B2

31.71

50.36

48.53-52.19

3.66

B142_1

B2

31.71

65.56

60.26-70.86

10.6

L-marker Position (cM)

C2

16.89

110.19

108.64-111.75

3.11

Satt277

107.59

Satt557

D2

6.52

25.28

24.52-26.04

1.52

Satt458

24.52

Satt135

26.05

H

16.28

50.72

47.23-54.22

6.99

Satt442

46.95

A404T_3

55.39

I

33.65

55.1

52.27-57.93

5.66

A955_1

51.99

Satt049

58.82

K

14.51

15.68

14.33-17.04

2.71

Satt242

14.35

Sat_119

17.11

M

8.80

9.28

6.25-12.31

6.06

Satt636

5.00

Satt201

13.56

O

14.37

40.19

38.71-41.67

2.96

Satt653

38.10

Satt347

42.30

Table 20. Meta-analysis results of 100-seed weight QTLs

Map L-marker distance

L-marker R-marker Position R-marker Position (cM) (cM)

LG

AIC value

Meta-QTL position(cM)

95% C.I. (cM)

B1

28.82

73.73

66.97-80.49

13.52

BLT043-1

66.77

C2

29.46

112.02

110.63-113.4

2.77

Bng164-1

C2

29.46

114.1

112.71-115.49

2.78

Satt289

F

24.04

17.5

3.50-26.6

23.1

Satt193

Satt332

80.89

110.14

Satt319

113.42

112.35

A397_1

116.72

3.42

Satt659

26.71

F

24.04

38.9

34.90-43.70

8.80

Satt160

33.19

Satt516

44.42

G

9.99

109.86

108.47-110.20

1.73

Sat_372

107.75

L120_1

110.45

L

26.02

4.06

2.29-5.84

3.55

Sat_408

1.31

Sle3_4s

6.19

L

26.02

10.23

8.32-12.14

3.82

BLT010_2

7.85

Satt182

14.03

L

23.53

32.22

30.66-33.78

3.12

Satt398

30.58

G214_1

34.14

L

23.53

38.16

36.77-39.55

2.78

A023_1

36.70

Satt462

41.00

L

13.09

101.7

93.18-110.22

17.04

Satt664

92.66

A802_2

111.07

Table 21. Meta-analysis results of lodging QTLs 4.3.3 Meta-analysis of soybean height QTLs A meta-analysis was carried out on the consensus QTL sites on 12 QTL clusters (Table 22 ). On LG B1 and LG K, 3 clusters were found, individually. The site with the minimal confidence interval was on LG K, with flanking markers Satt441 and Satt552 at 41.00 cM and 46.00cM, the C.I. was 0.24cM.

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Soybean - Molecular Aspects of Breeding

Lmarker

L-marker Position (cM)

Rmarker

R-marker Position (cM)

3.72

Satt426

28.00

Sat_156

35.00

4.33

Sat_149

54.00

Satt298

65.00

81.87-93.01

11.14

Sat_095

81.00

Satt665

96.00 112.00

LG

AIC value

Meta-QTL position(cM)

95% C.I. (cM)

Map distance

B1

105.37

32.61

30.75-34.47

B1

105.37

56.42

54.25-58.58

B1

105.37

87.44

C2

18.04

105.05

102.33-107.78

5.45

Satt665

102.00

Satt365

D1a

16.92

49.04

48.20-49.88

1.68

Satt342

48.00

Sat_159

50.00

F

27.28

111.34

105.70-116.98

11.28

Sat_197

104.00

Satt218

118.00

G

21.53

10.08

4.12-16.04

11.92

Sat_168

3.90

Satt217

18.00

K

19.30

38.93

36.99-40.86

3.87

Satt137

37.00

Satt178

41.00

K

19.30

41.65

40.89-42.41

1.52

Satt178

41.00

Satt555

43.00

K

19.30

46.32

46.20-46.44

0.24

Satt441

41.00

Satt552

46.00

M

32.35

18.58

11.08-26.08

15.00

Satt590

7.80

Satt567

33.00

M

32.35

42.47

37.06-47.88

10.82

Satt540

34.00

Sat_244

49.00

Table 22. Meta-analysis results of soybean height QTLs 4.4 Meta-analysis of soybean growth stage QTLs A meta-analysis was carried out on the consensus QTL sites on 7 QTL clusters for R1 period (Table 23), 2 QTL clusters for R8 period(Table 24), and 10 QTL clusters for multiple period(Table 25). Three main sites related with R1 period were on linkage group J, with flanking left markers Satt076, A461_1, and K385_1 at 61.40cM, 87.90cM and 101.30cM, corresponding right markers L050_8, Bng095_1, and Sat_245 at 72.70cM, 100.40cM, and 115.10cM, the C.I. were 10.61cM, 8.66cM, and 10.60cM. The site with the minimal confidence interval was on LG C2, with flanking markers Satt365 and Satt658 at 111.70 cM and 113.60cM, the C.I. was 1.63cM. One site related with R8 period was on LG C2, with flanking markers A397_1 and Sat_263 at 116.70 cM and 118.80cM, the C.I. was 0.90cM. The other site related with R8 period was on LG L, with flanking markers Satt156 and Satt678 at 56.10 cM and 70.20cM, the C.I. was 9.64cM. LG

AIC Meta-QTL 95% C.I. value position(cM) (cM)

C2

24.64

Map distance

L-marker

L-marker Position (cM)

R-marker

R-marker Position (cM)

106.39

98.1-107.62

9.52

Satt363

98.10

Satt277

107.60

1.63

Satt365

111.70

Satt658

113.60

30.20

Satt540

35.80 27.30

C2

24.64

112.75

111.98113.61

M

20.03

33.48

32.48-34.47

1.99

Ts

M

20.23

18.58

11.08-26.08

15.00

Satt590

7.80

Mng339_1

L

47.46

67.38

62.08-72.69

10.61

Satt076

61.40

L050_8

72.70

L

47.46

93.26

88.93-97.59

8.66

A461_1

87.90

Bng095_1

100.40

L

47.46

107.24

101.94112.54

10.60

K385_1

101.30

Sat_245

115.10

Table 23. Meta-analysis results of R1 period QTLs

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Integration of Major QTLs of Important Agronomic Traits in Soybean

For multiple periods, some sites were only related with reproductive periods, such as Mul1, Mul2, Mul3, Mul4, Mul9, and Mul10; some site were only related with vegetative periods, such as Mul5 and Mul6; some site were related with both reproductive periods and vegetative periods. Mul3 was very special for its long-term effect on the six of eight reproductive periods. L-marker R-marker Map L-marker R-marker distance Position(cM) Position(cM)

L AIC G value

Meta-QTL position(cM)

95% C.I. (cM)

C2 28.03

117.66

117.21-118.11

0.90

A397_1

116.70

Sat_263

118.80

64.19

59.37-69.01

9.64

Satt156

56.10

Satt678

70.20

L

16.87

Table 24. Meta-analysis results of R8 period QTLs Locus name Mul1 Mul2 Mul3 Mul4 Mul5 Mul6 Mul7 Mul8 Mul9 Mul10

LG

Position(cM)

Flanking Marker

Period

C1 C1 D1a D1a D1b D1b D1b D1b F J

105.06 112.43 55.89 63.76 67.09 73.41 78.02 80.84 26.98 43.01

Sat_076-K011_3 Satt365-Satt658 Satt515-Sat_201 Satt343-Satt507 A605_1-Sat_423 Satt290 Bng047_1-Sat_169 Satt644-Satt041 Satt659-Satt206 Satt380

R1, R7 R1, R8 R1, R2, R3, R4, R5, R7 R2, R4, R6, R8 V8, V9 V11, V12 V13, V14, R3, R4, R5 V18, R1, R3 R1, R3, R7 R1, R3, R7

Table 25. Meta-analysis results of multiple period QTLs

5. Discussion Soymap2, the reference map A new public map, soymap2, was constructed by Song (2004). Five soybean genetic maps, including molecular genetic maps of two F2 populations, A81-356022 × PI468916 and Clark × Harosoy, and three RIL populations, Minsoy × Noir1, Minsoy × Archer, and RILs of Noir 1 × Archer, were integrated. The public map contains 20 LGs and 1,849 markers, including 709 restriction fragment length polymorphism (RFLP) markers, 1,015 Simple Sequence Repeat (SSR) markers, 73 random amplified polymorphic DNA (RAPD) markers, six amplified fragment length polymorphism (AFLP) markers, and 46 markers of other types. This integrated map shows a very high density of SSR and RFLP markers, commonly used to map QTLs. Thus, QTLs from these maps could be easily projected onto the public map. Using of meta-analysis in MAS MAS is an important strategy for crop improvement. Recently, MAS has been successfully used to increase the quality and yield of wheat (Romagosa. 1999) and rice (Wang. 2004). However, due to its validity, cost, and the low number of markers, MAS has not been widely applied in crops. The accuracy of mapping QTLs decides the efficiency of gene discovery and cloning. Meta-analysis is an important tool in linkage analysis, optimize QTL, shrink the confidence interval, and improve the accuracy and validity of QTL position (Löffler, 2009), and is of

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particular relevance for the validation of known QTL. QTL location is affected by many factors, including genetic background, population size and analytical method, so a single study can only be taken as suggestive, unless it is based on a large enough set of experiments. Where the CI is large, it is difficult to distinguish between the presence of a single locus and the presence of two (or more) loci. The meta-analysis approach, as developed by Goffinet and Gerber (2000), however does provide a means to alleviate the extent of this uncertainty, since it improves the capacity to identify the true number of QTL present, and the precision of their location by reducing their associated CI. QTL associated with early maturity in bread wheat have recently been identified using a meta-analysisbased approach (Hanocq, 2007), and similarly, resistance to gray leaf spot in maize has been genetically defined by the integration of >50 QTL (Shi, 2007). The proportion of the phenotypic variance explained by a given QTL (its R2 value) is the most important parameter in deciding whether marker assistance can be more efficient than conventional phenotypic selection alone (Bernardo 2001; Bernardo and Charcosset 2006). The molecular markers in the consensus QTL could be used for individual selection in the original mapping population. In addition, some important intervals could be enhanced by backcrossing for QTL fine mapping. However, some key aspects of MAS still require improvement. The first is how to evaluate the contribution ratio of the allele for a special locus. For some populations, the allele is major, but for others it is minor. The second aspect is how to build a practical model for MAS or molecular design, which is important for the application of markers. We hope that the meta-analysis reported here will guide the choice of QTL targeted for marker assisted selection, and it was the foundation for the process of acquiring QTL-related genes in soybean. Use of bioinformatics tools in mining candidate genes With the advent of soybean genomic information and bioinformatics tools, finding consensus QTL intervals in the corresponding physical map is easier, particularly for mining candidate genes (Lv et al. 2008). Bioinformatics tools are important in the process leading from QTL to the quantitative trait gene, or QTG. Wang and Paigen (2002) found that 18 of the 22 human high-density lipoprotein-cholesterol (HDL-C) QTLs were within the murine HDL-C QTLs, suggesting that murine QTLs for HDL-C levels may have homologous locations in humans and that their underlying genes may be appropriate for testing in humans. GENSCAN is a general-purpose gene identification program that analyzes genomic DNA sequences from a variety of organisms, including humans, other vertebrates, invertebrates, and plants. The InterProScan server analyzes sequences (cDNA, protein) with respect to international GO terms. In this study, traditional QTLs were integrated, and some important genes related to the important agronomic traits could be mined in the future.

6. Conclusion In this chapter, the QTLs of 12 important agronomic traits in soybean were integrated by meta-analysis. For seed quality traits, 25 consensus QTLs of oil content were extracted from 130 initial research QTLs, 23 consensus QTLs of protein concentration from 107 QTLs, 23 consensus QTLs of fatty acid from 83 QTLs, 111 consensus QTLs of amino acid content from 111 QTLs, 10 consensus QTLs of isoflavone content from 70 QTLs. For biotic stress resistance traits, 23 consensus QTLs of fungal disease resistance were extracted from 107 initial research QTLs, 14 consensus QTLs of insect resistance from 81 QTLs, 16 consensus

Integration of Major QTLs of Important Agronomic Traits in Soybean

113

QTLs of soybean cyst nematode resistance from 135 QTLs. For yield trait, 6 consensus QTLs of 100-seed weight were extracted from 78 initial research QTLs, 11 consensus QTLs of lodging from 59 QTLs, 12 consensus QTLs of plant height from 93 QTLs. For soybean growth stages, 7 consensus QTLs were extracted from 98 initial research QTLs

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Meksem, K. (1999). Clustering among loci underlying soybean resistance to Fusarium solani, SDS and SCN in near-isogenic lines. Theoretical and Applied Genetics, 9, 11311142. ISSN: 0040-5752 Meksem, K. (2001a). Genomic regions that underlie soybean seed isoflavone content. Journal of Biomedicine and Biotechnology, 1, 1, 38-44. ISSN 1110-7243 Meksem, K. (2001b). ‘Forrest’ resistance to the soybean cyst nematode is bigenic: saturation mapping of the Rhg1 and Rhg4 loci. Theoretical and Applied Genetics, 103, 5, 710717. ISSN: 0040-5752 Meng, X. (2003). QTL mapping genes conferring resistance to race 4 of soybean cyst nematode in soybean ZDD2315 (Glycine max (L) Merr.) based on public molecular genetic linkage map. Molecular Plant Breeding, 1, 1, 6-21. ISSN 1672-416X Mian, MAR. (1999). Molecular mapping of the Rcs3 gene for resistance to frogeye leaf spot in soybean. Crop Science, 39, 1687-1691. ISSN: 0011-183X Mian, RMA. (2008). Genetic linkage mapping of the soybean aphid resistance gene in PI 243540. Theoretical and Applied Genetics, 117, 955–962. ISSN: 0040-5752 Monteros, MJ. (2007). Mapping and confirmation of the ‘Hyuuga’ red-brown lesion resistance gene for Asian soybean rust. Crop Science, 47, 829-836. ISSN: 0011-183X Naoki, Y. (2001). An informative linkage map of soybean reveals QTLs for flowering time, leaflet morphology and regions of segregation distortion. DNA Research, 8, 61-72. ISSN 1340-2838 Narvel, JM. (2001). A retrospective DNA marker assessment of the development of insect resistant soybean. Crop Science, 41, 6, 1931−1939. ISSN: 0011-183X Njiti, VN. (2002). Common loci underlie field resistance to soybean sudden death syndrome in Forrest, Pyramid, Essex, and Douglas. Theoretical and Applied Genetics, 104, 294-300. ISSN: 0040-5752 Orf, JH. (1999).Genetics of soybean agronomic traits: I. Comparison of three related recombinant inbred populations. Crop Science, 39, 6, 1642-1651. ISSN: 0011-183X Panthee, DR. (2005). Quantitative Trait Loci for Seed Protein and Oil Concentration, and Seed Size in Soybean. Crop Science, 45, 2015-2022. ISSN: 0011-183X Panthee, DR. (2006a). Modifier QTL for fatty acid composition in soybean oil. Euphytica, 152, 67–73. ISSN 0014-2336 Panthee, DR. (2006b). Genomic regions associated with amino acids composition in soybean seeds. Molecular Breeding, 17, 79-89. ISSN 1380-3743 Panthee, DR. (2006c). Quantitative trait loci controlling sulfur containing amino acids, methionine and cysteine, in soybean seeds. Theoretical and Applied Genetics, 112, 546-553. ISSN: 0040-5752 Patzoldt, ME. (2005a). Characterization of resistance to brown stem rot of soybean in five accessions from central China. Crop Science, 45, 1092-1095. ISSN: 0011-183X Patzoldt, ME. (2005b). Localization of a quantitative trait locus providing brown stem rot resistance in the soybean cultivar bell. Crop Science, 45, 1241-1248. ISSN: 0011-183X Prabhu, RR. (1999). Selecting soybean cultivars for dual resistance to soybean cyst nematode and sudden death syndrome using two DNA markers. Crop Science, 39, 4, 982-987. ISSN: 0011-183X Qiu, BX. (1999). RFLP markers associated with soybean cyst nematode resistance and seed composition in a 'Peking' x 'Essex' population. Theoretical and Applied Genetics, 98, 356-364. ISSN: 0040-5752

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Rector, BG. (1998). Identification of molecular markers linked to quantitative trait loci for soybean resistance to corn earworm. Theoretical and Applied Genetics, 96, 786−790. ISSN: 0040-5752 Rector, BG. (2000). Quantitative trait loci for antibiosis resistance to corn earworm in soybean. Crop Science, 40, 1, 233−238. ISSN: 0011-183X Romagosa, I. (1999). Verification of yield QTL through molecular marker assisted selection responses in a barley cross. Molecular Breeding, 5, 143-152. ISSN 1380-3743 Schuster, I. (2001). Identification of a new major QTL associated with resistance to soybean cyst nematode. Theoretical and Applied Genetics, 2001, 102(1): 91-96. ISSN: 00405752 Sebolt, AM. (2000). Analysis of a quantitative trait locus allele from wild soybean that increases seed protein concentration in soybean. Crop Science, 40, 5, 1438-1444. ISSN: 0011-183X Shan, DP. (2008). Epistatic Effects of QTLs and QE Interaction Effects on Oil Content in Soybean. Acta Agronomica Sinica, 34, 6, 952-957. ISSN 0496-3490 Shan, DP. (2009). Epistatic effects of QTLs and QE Interaction effects on protein content in soybean. Acta Agronomica Sinica, 35, 1, 41-47. ISSN 0496-3490 Shi, LY. (2007). Comparative QTL mapping of resistance to gray leaf spot in maize based on bioinformatics. Scientia Agricultura Sinica, 6, 12, 1411-1419. ISSN 0578-1752 Song, QJ. (2004). A new integrated genetic linkage map of the soybean. Theoretical and Applied Genetics, 109, 122-128. ISSN: 0040-5752 Specht, JE. (2001). Soybean response to water: a QTL analysis of drought tolerance. Crop Science, 41, 2, 493-509. ISSN: 0011-183X Spencer, M. (2004). Molecular markers associated with linolenic acid content in soybean. Journal of the American Oil Chemists' Society, 81, 559–562. ISSN: 0003-021X Tajuddin, T. (2003). Analysis of quantitative trait loci for protein and lipid contents in soybean seeds using recombinant inbred lines. Breeding Science, 53, 133-140. ISSN 1344-7610 Tasma, M. (2001). Mapping genetic loci for flowering time, maturity, and photoperiod insensitivity in soybean. Molecular Breeding, 8, 25-35. ISSN 1380-3743 Terry, LI. (1999). Insect resistance in recombinant inbred soybean lines derived from nonresistant parents. Entomologia Experimentalis et Applicata, 91, 465–476. ISSN: 0013-8703 Terry, LI. (2000). Soybean quantitative trait loci for resistance to insects. Crop Science, 40, 2, 375−382. ISSN: 0011-183X Vierling, RA. (1996). Association of RFLP markers with loci conferring broad-based resistance to the soybean cyst nematode (Heterodera glycines). Theoretical and Applied Genetics, 92, 1, 83-86. ISSN: 0040-5752 Walker, DR. (2004). A QTL that enhances and broadens Bt insect resistance in soybean. Theoretical and Applied Genetics, 109, 5, 1051−1057. ISSN: 0040-5752 Wang, D. (2001). Loci underlying resistance to Race 3 of soybean cyst nematode in Glycine soja plant introduction 468916. Theoretical and Applied Genetics, 103, 4, 561-566. ISSN: 0040-5752 Wang, D. (2004). Identification of putative QTL that underlie yield in interspecific soybean backcross populations. Theoretical and Applied Genetics, 108, 458-467. ISSN: 00405752 Wang, H. (2007). A Sensitive molecular marker SSR associated with resistant gene to Heterodera Glycines. Soybean science, 26, 2, 204-207. ISSN 1000-9841

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Wang, XS. (2002). Quantitative trait loci and candidate genes regulating HDL cholesterol: a murine chromosome map. Arteriosclerosis, Thrombosis, and Vascular Biology, 22, 9, 1390-1401. ISSN 1049-8834 Wang, XZ. (2007). QTL analysis of seed and pod traits in soybean RIL population. Acta Agronomica Sinica, 33, 3, 441-448. ISSN 0496-3490 Wang, XZ. (2008). Map construction and QTL analysis of oil content in soybean. Chinese Journal Of Oil Crop Science, 30, 3, 272-278. ISSN 1007-9084 Wang, YG. (2004). Molecular marker assisted selection for yield enhancing genes in the progeny of Minghui63 ×Orufipogon. Scientia Agricultura Sinica, 103, 75-83. ISSN 0578-1752 Weng, CH. (2007). A quantitative trait locus influencing tolerance to Phytophthora root rot in the soybean cultivar ‘Conrad’. Euphytica, 158, 81-86. ISSN 0014-2336 Wu, XL. (2001). QTL mapping of some agronomic traits of soybean. Journal of Genetics and Genomics, 28, 10, 947-955. ISSN: 1673-8527 Xin, DW. (2007). Analysis of quantitative trait loci underlying the period of vegetative growth stages in soybean (Glycine max [L.] Merr.). Molecular Plant Breeding, 5, 5, 639-647. ISSN 1672-416X Xin, DW. (2008). Analysis of quantitative trait loci underlying the period of reproductive growth stages in soybean (Glycine max [L.] Merr.). Euphytica, 162, 2, 155-165. ISSN 0014-2336 Xu, P. (2007). QTL positioning of oil content in soybean. Hereditas, 29, 1, 92-96. ISSN 1673-8527 Yang, Z. (2004). Construction of genetic map and QTL analysis for some agronomic traits in soybean. Acta Agronomica Sinica, 30, 413-418. ISSN 0496-3490 Yarmilla, Reinprecht. (2006). Seed and agronomic QTL in low linolenic acisd lipoxygenasefree soybean (Glycine max (L.)Merrill ) germplasms. Genome, 49, 1510-1527. ISSN 0831-2796 Yue, P. (2001a). Mapping resistance to multiple races of Heterodera glycines in soybean PI 89772. Crop Science, 41, 5, 1589-1595. ISSN: 0011-183X Yue, P. (2001b). Molecular characterization of resistance to Heterodera glycines in soybean PI 438489B. Theoretical and Applied Genetics, 102, 6-7, 921-928. ISSN: 0040-5752 Zhang, GR. (2009). Molecular mapping of soybean aphid resistance genes in PI 567541B. Theoretical and Applied Genetics, 118, 473–482. ISSN: 0040-5752 Zhang, WH. (2004). Analysis of resistant gene against Cercospora sojina Race 1 in soybean with SSR markers. Soybean Science, 23, 169-173. ISSN 1000-9841 Zhang, WK. (2004). QTL mapping of ten agronomic traits on the soybean (Glycine max L. Merr.) genetic map and their association with EST markers. Theoretical and Applied Genetics, 108, 1131-1139. ISSN: 0040-5752 Zhang, ZC. (2004). QTL mapping seed oil and protein content of soybean. Soybean Science, 23, 2, 81-85. ISSN 1000-9841 Zhao, G. (2005). Inheritance and genetic mapping of resistance to rhizoctonia root and hypocotyl rot in soybean. Crop Science, 45, 1441-1447. ISSN: 0011-183X Zhao, GY. (2008). Identification of QTL underlying the resistance of soybean to pod borer, Leguminivora glycinivorella (Mats.) obraztsov, and correlations with plant, pod and seed traits. Euphytica, 164, 275–282. ISSN 0014-2336 Zheng, Yongzhan. (2006). QTL Mapping for fat and fatty acid composition contents in soybean. Acta Agronomica Sinica, 32, 12, 1823-1830. ISSN 0496-3490 Zhou, R. (2009). QTL analysis of lodging and related traits in soybean. Acta Agronomica Sinica, 35, 1, 57-65. ISSN 0496-3490

6 A Versatile Soybean Recombinant Inbred Line Population Segregating for Low Linolenic Acid and Lipoxygenase Nulls - Molecular Characterization and Utility for Soymilk and Bioproduct Production Yarmilla Reinprecht, Shun-Yan Luk-Labey and K. Peter Pauls University of Guelph, Plant Agriculture, 50 Stone Road E, Guelph, ON N1G 2W1 Canada 1. Introduction The chapter aims to: (a) review the development of a soybean recombinant inbred line (RIL) population obtained from the RG10 x OX948 cross that is segregating for low linolenic acid (LLA) and lipoxygenase nulls (3lx), (b) present the molecular characterization of the LLA and 3lx traits and (c) discuss the applicability of the population for soymilk and biocomposite production. The chapter is organized as follows. Section 2 describes development and evaluation of the RG10 x OX948 RIL population, inheritance of LLA and 3lx traits and the development of novel low linolenic acid, lipoxygenase free (LLA.3lx) germplasm. Section 3 describes the molecular characterization of the LLA and 3lx traits derived from RG10 and OX948, respectively. The versatility of the RIL population for different applications, including soymilk and bioproduct production are presented in Section 4, which illustrates that there is potential for utilizing the whole soybean plant. The conclusion summarizes the results and discusses possible future uses of the RG10 x OX948 RIL population. Problem - Oxidation of linolenic acid (LA) is catalyzed by lipoxygenase (LX; linoleate: oxygen oxidoreductase; EC 1.13.11.12) and is associated with off-flavours of soybean products. The hydroperoxides that are produced and their breakdown products impart undesirable grassy-beany and bitter flavours on soybean products (Rackis et al., 1979). Most soybean cultivars contain 70 to 90 g kg-1 LA and approximately 20 g kg-1 of the total seed protein is LX. A possible solution to soybean stability problems is to genetically eliminate seed LX and reduce the content of LA. A number of soybean cultivars with low LA content (LLA; < 40 g kg-1 LA) or seed LX nulls (Hammond et al., 1972; Hildebrand & Hymowitz, 1982; Wilcox, 1985; White, 2000) have been developed. It was shown that oil extracted from LLA lines is more stable than oil extracted from conventional soybean, needs less hydrogenation and consequently, contains less unhealthy trans fatty acids (Mounts et al., 1988), which may reduce the risk of heart disease (Hayakawa et al., 2000). Similarly, protein products from lx3 soybean have more favourable flavour profiles (King et al., 1998). It has been suggested that a combination of seed LX nulls with LLA content might lead to even

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better tasting soybean protein products and oil with improved oxidative stability (Davies & Nielsen, 1986). However, oil extracted from a line combining LX2 and LX3 nulls with less than 30 g kg-1 LA content did not show improved oxidative stability (Shen et al., 1996). To explore the potential for improving oil stability in soybean further we combined LX triple null (lx1lx2lx3) from line OX948 with LLA content from lines RG10 (fan-b) and PI 361088B [fan(PI 361088B)] into new LLA.3lx soybean lines (Reinprecht et al., 2005; 2006a). Inheritance - Different patterns of inheritance for LA have been reported including quantitative inheritance (Graef et al., 1988) or a combination of major and minor genes (Fehr et al., 1992). The LLA content in RG10 is simply inherited and controlled by homozygous recessive alleles with additive effects at the Fan locus (Stojsin et al., 1998). Different types of gene action for LA content, ranging from codominance to complete dominance, have been reported for different crosses (Rahman et al., 1998). Also, depending of cross, complete maternal inheritance of LA content (Martin et al., 1983), partial maternal effects (Graef et al., 1988), or the lack of a maternal effect (Rahman et al., 1998) have been observed. In addition, environmental conditions, especially temperature were reported to have effect on unsaturated fatty acid contents (Wolf et al., 1982). Similarly, inheritance of seed LX is simple. A single distinct gene encodes each of the three seed isozymes. Nulls for LX are controlled by single recessive loci, named lx1, lx2 and lx3 (Hildebrand & Hymowitz, 1982; Kitamura et al., 1983; Davies & Nielsen, 1986). Lx1 and Lx2 are tightly linked whereas the inheritance of Lx3 is independent of Lx1 and Lx2 (Davies & Nielsen, 1986), so that in 3lx mutants, a two-gene inheritance pattern has been observed (Hajika et al., 1992). No maternal or cytoplasmic effects were reported (Hildebrand & Hymowitz, 1982). The new LLA.3lx soybean lines were used to study the inheritance of LA and LX genes and to test for possible interactions between these traits (Reinprecht et al., 2005; 2006a). Molecular basis of LLA and lx3 traits - In soybean seed, LA is synthesized predominantly in the endoplasmic reticulum (ER) by desaturation of linoleic acid (Cherif et al., 1975). The reaction is catalyzed by cytoplasmic, membrane-bound, ω-3 fatty acid desaturase (E.C. 1.14.99.-), which introduces the third double bond in linoleic acid at the ω-3 position. It was suggested that three microsomal ω-3 fatty acid desaturase genes (GmFad3A, GmFad3B and GmFad3C) contribute to LA levels in soybean seed, with the GmFad3A (GmFad3-1b) being the most abundant (Bilyeu et al., 2003; Anai et al., 2005). Therefore, mutations in these genes could reduce the LA concentration in soybean seeds (Byrum et al., 1997; Bilyeu et al., 2005). A number of soybean breeding lines and cultivars with LLA content have been developed by mutagenesis and conventional breeding. The LA content in an ethyl methane sulfonate (EMS) mutant line C1640 (~34 g kg-1, Wilcox et al., 1984) is controlled by a single recessive fan(C1640) allele at a major Fan locus (Wilcox & Cavins, 1985), which encodes ω-3 fatty acid desaturase (Brummer et al., 1995). The Fad3 gene, coding for ω-3 fatty acid desaturase, was mapped to the same region (Byrum et al., 1995). The molecular basis of the LLA trait has been described in different backgrounds. The fan allele from the LLA line A5, initially associated with the full or partial deletion of an unknown microsomal ω-3 desaturase gene (Byrum et al., 1997), was later assigned to the GmFad3A gene (Bilyeu et al., 2003). The mutated gene from the line M24 (X-ray mutant) corresponds to GmFad3B (GmFad3-1a) at the Fanx locus (Anai et al., 2005). Soybean line A29 has mutations in all three ω-3 fatty acid desaturases (GmFad3A, GmFad3B and GmFad3C) and has a LA content of 10 g kg-1 (Bilyeu et al., 2006). RG10 is a LLA mutant line produced by EMS treatment of C1640. The LLA content in RG10 (less than 25 g kg-1) is simply inherited and controlled by homozygous

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recessive alleles at the Fan locus with additive effects. The RG10 allele was designated as fanb (for a very low content of LA) by Stojsin et al. (1998). The objective of this study was to determine the molecular basis of the LLA trait in soybean mutant line RG10, developed by two rounds of EMS treatment. Mature soybean seeds contain three LX isozymes, namely, LX1, LX2 and LX3 (Axelrod et al., 1981). The isozymes have different pH optima, substrate specificities and reaction products (Feussner & Wasternack, 2002). Spontaneous mutants for single LX isozymes have been identified (Hildebrand and Hymowitz 1981; Kitamura, 1984) and a number of single, double (Kitamura et al., 1985) and 3lx mutants were developed by gamma (γ)-irradiation (Hajika et al., 1991). The molecular basis for three seed LX nulls has been described in different backgrounds. Wang et al. (1994) identified a single amino acid replacement at H532Q (corresponds to H504 in LX1) in two triple null γ-mutant lines (Kyushu 111 and the K line) that resulted in an inactive LX2 isozyme. Two null alleles have been identified for the Lx3 gene. Two single nucleotide substitutions in the promoter region of Lx3 (mutation 2: C to T at -636 and mutation 1: T to A at -585) in Kyushu 111 and the K line resulted in a complete null for that isozyme (Wang et al., 1995). The lines PI 205085, PI 417458 (Lx1Lx2lx3) and Jinpumkong 2 (lx1lx2lx3) have an lx3-a allele, which is a frame shift mutation (single G deletion at the position 101 in exon 1) that introduces a stop codon and results in premature termination of protein translation (Lenis et al., 2010). Three null alleles have been reported for the Lx1 gene. The lx1-a allele (PI 408251) is a 74 bp deletion in exon 8 at position 2752, which introduces a stop codon and terminates protein synthesis after 524 amino acids. In addition to the same 74 bp deletion, the lx1-c allele (Jinpumkong 2) contains seven substitutions and 3 bp deletion at the 5’ end of the gene. A nonsense mutation C2880A in the lx1-b allele (PI 133226) results in an S568STOP change (Lenis et al., 2010). The objective of this work was to determine the molecular basis of the seed LX nulls in soybean mutant line OX948. Many GenBank records of ω-3 fatty acid desaturase (Fad3) and seed LX (Lx1, Lx2 and Lx3) genes exist. Recent release of a complete draft of soybean genome sequence opens new insights into genome organization of this ancient paleopolyploid (Schmutz et al., 2010). Four homologous regions containing 19 LX genes are present in the soybean genome (Shin et al., 2008). Similarly, homology between GmFad3-1a (Fad3B) and GmFad3-1b (Fad3A) and between GmFad3-2a (Fad3C) and GmFad3-2b (‘Fad3D’) suggests that the ω-3 fatty acid desaturase gene family in soybean may have developed through gene duplication (Anai et al., 2005). LA and LX markers - Conventional methods like gas liquid chromatography (Bannon et al., 1987) and colorimetric assays (Suda et al., 1995), are convenient for screening lines for LA content and seed LX status in soybean breeding programs focussed on selecting cultivars with improved flavour. Both procedures can be performed on a small piece of the seed, leaving the rest of the seed for planting. However, the assays are time consuming and the colorimetric LX assays are subjective and cannot distinguish heterozygotes. In contrast, screening procedures based on codominant, gene-specific, markers for LLA and 3lx traits could increase the precision and efficiency of breeding for LLA.3lx soybean since the markers, which are environmentally neutral, can be determined quickly and accurately using DNA extracted from any small tissue sample. The use of marker-assisted selection (MAS) can accelerate and simplify breeding soybean with improved flavour. Kim et al. (2004) developed a single nucleotide polymorphism (SNP) marker for the Lx2 gene. Spencer et al. (2004) developed a simple sequence repeat (SSR) marker Satt534, while Sauer et al. (2008) developed Fad3A gene-based markers for LA content. Working with the LLA.3lx RG10 x OX948 population, our group developed markers for LLA (SSR Satt534 and gene-

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based Fad3i6) and 3lx (Lox1-3 and Lox3-HaeIII) (Reinprecht, 2002; Reinprecht et al., 2006b), which have been used to simultaneously select for LLA and 3lx (lx1/lx2 and lx3) phenotypes, thus accelerating selection for soybean lines for improved soymilk stability and flavour (Luk, 2006). The objectives of this work were to characterize previously developed gene-based markers and to develop additional markers based on mutations in newly sequenced ω-3 fatty acid desaturase and LX genes. Use of RIL population in soymilk study - Soymilk is a highly nutritious beverage but faces some consumer acceptance barriers because of objectionable flavours and odours (TorresPenaranda & Reitmeier, 2001). It is made by grinding water-soaked soybeans with additional water. The liquid is separated from the okara (ground soybean) by filtration and then heat processed (Liu, 1997). The compounds that give soy its poor taste are believed to be breakdown products of the hydroperoxides formed during oxidation of polyunsaturated fatty acids, like linolenic acid (Dutton et al., 1951). Hydroperoxide formation is catalyzed by seed LX (Furata et al., 1996; Wilson, 1996). Hydrogenation has been used to improve both the flavour and stability of soybean oil (Dutton et al., 1951; Mounts et al., 1994) but studies have shown that oils produced by this process contain trans fatty acid isomers, which are associated with an increased risk of coronary heart disease when consumed for an extended period of time (Zock & Katan, 1997). Studies on oil stability and flavour show that soybean oils with reduced LA content compared favourably to oils derived from conventional soybean cultivars (Mounts et al., 1988; Mounts et al., 1994). LX-free lines have also been shown to produce less of the volatiles responsible for the generation of off-flavours (Kobayashi et al., 1995; Furata et al., 1996). The level of LA in soybean oil is typically 8% (or 80 g kg-1) but LLA soybean lines (~ 2%) have been produced that are comparable to conventional soybean lines for their agronomic and seed traits (Ross et al., 2000; Reinprecht et al 2005). It has been also suggested that the elimination of lipoxygenase from soy products would make them blander and more acceptable to consumers (Wilson, 1996). Lines from the LLA.3lx soybean RIL population were used to test the effects of LLA and lx3 traits on lipid oxidation and soymilk flavour (Luk, 2006). Use of RIL population in composite study - Soybean is grown for it high value oil (20%) and protein (40%) seed. However, residues (stem and leaf) left after harvesting the seed are potential sources of significant amounts of low cost plant fibers. For every ton of grain harvested there is 3-5 times that weight in stem fibers. Although annually renewable and available in abundance, agricultural residues (including soybean) are of limited value at present and are usually discarded and left in the field to decompose (Sticklen, 2006). The use of crop residues for industrial applications could be an additional source of revenue for farmers. If only a portion (approximately 30%) of the stems are collected this can be done without adversely affecting soil fertility (Lindstrom, 1986). Properties such as good mechanical performance, good formability, biodegradability, high sound absorption, low abrasiveness, low dermal and respiratory irritability and low density make plant fibers suitable for use as fillers and reinforcements in composite materials. Use of these materials has the potential to reduce the environmental impact of composite manufacture and reduce fuel consumption. Demands for lightweight car parts and good recycling possibilities (“green” car) are the main reasons for investigating the potential of using biofibers in the automotive industry. However, because of weak bonding between the fibers (hydrophilic) and the polymer matrices (hydrophobic), moisture sensitivity, odour emission, the relatively low processing temperatures that can be used and variation in the quality of biofibers the use of composites with plant fibers currently has limited applications to the car interior.

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Plant fibers are composed of cell wall materials. As a dicot, soybean has a type I primary cell wall, which consists of cellulose microfibrils buried in a matrix composed of hemicellulose (xyloglucan), pectin (homogalacturonan and rhamnogalacturonan) and protein (extensins and atabinogalactans). The interactions between the different polysaccharides provide the strength and flexibility of the cell wall (Hartholt et al., 2010). Secondary wall (synthesized after cell stops growing) contains mainly cellulose, hemicellulose and lignin (Yokoyama & Nishitani, 2004). Cellulose is a linear polymer of hydrogen-bonded β-(1,4)-linked glucose molecules organized in parallel crystalline layers, which form 3 nm thick microfibrils. Hydrogen bonds between different layers of the polysaccharides contribute to the resistance of crystalline cellulose to degradation (Sticklen, 2008). Primary cell wall microfibrils consist of approximately 8000 glucose molecules, while secondary cell wall may contain 15000 glucose molecules (Mutwil et al., 2008). Physical properties such as the crystalline state, degree of crystallinity and molecular weight are highly variable. Cellulose is synthesized in the plasma membrane by cellulose synthases (CesA) multimeric complexes arranged as hexagonal rosettes, each producing one microfibril of 36 glucan chains. Arabidopsis thaliana has at least 10 CesA genes with cell-specific expression patterns and functions. CesA1, CesA3 and CesA6 encode enzymes for primary wall cellulose biosynthesis, while CesA4, CesA7 and CesA8 are expressed during secondary wall cellulose biosynthesis. Interactions between CesA and the cytoskeleton have important effects on cellulose fibril orientation and length (Somerville, 2006; Mutwil et al., 2008; Penning et al., 2009). While cellulose has a relatively simple chemical structure, which is same for all plant species, hemicellulose is more complex. Hemicelluloses encompass a heterogeneous group of polysaccharides (including xylans, xyloglucans and (gluco)mannans) that have β-(1,4)linked backbones (composed of various 5- and 6-carbon sugars such as arabinose, galactose, glucose, mannose and xylose) with equatorial configurations. Its structure differs among plant species and cell types. Dicot primary cell walls are composed mainly of xyloglucan (20-25% w/w) with small amounts of glucuronoarabinoxylan (5%) and (gluco)mannan (35%). Glucuronoxylan is the major component (20-30%) of secondary cell walls with the small amounts of xyloglucan, (gluco)mannan (2-5%) and galactoglucomannan (0-3%) (Scheller & Ulvskov, 2010). Hemicelluloses are synthesized in the Golgi membranes by glycosyltransferases. The enzymes required for xyloglucan biosynthesis include α-Larabinofuranosidase, xyloglucan endotransglucosylase, endo-xyloglucan transferase, βmannosidase and β-galactosidase. Pectin is structurally and functionally the most complex polysaccharide and it forms a gel matrix in the plant primary cell wall. Pectins are composed of highly complex family of polysaccharides rich in D-galacuronic acid. Cell walls contain different pectic polysaccharides including homogalacturonan (HG), xylogalacturonan (XGA), apiogalacturonan, rhamnogalacturonan (RGI) and rhamnogalacturonan II (RGII). Pectin is synthesized in the Golgi membranes and about 67 different glycosyltransferases, methyltransferases and acetyltransferases are required for these processes (Mohnen et al., 2008). Lignin is a complex aromatic polymer composed of three major phenolic compounds, namely: p-coumoryl alcohol (H lignin), coniferyl alcohol (G lignin) and sinapyl alcohol (S lignin). Monolignol biosynthesis starts from the amino acid phenylalanine and proceeds through number of ring and side-chain modifications catalyzed by dozens of enzymes. The three monolignols differ from each other only by their degree of metoxylation. Peroxidase, laccase and dirigent proteins are involved in monolignol polymerization. The ratio of

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monolignols within the polymer varies between plant species, different tissues and cell wall layers (Boerjan et al., 2003). Cell walls also contain proline-rich proteins, hydroxyproline-rich glycoproteins, arabinogalactan proteins, expansins, germin-like proteins, extensins, actin, 14-3-3-like proteins and laccase. Some of the cell wall modifying enzymes include: endo-1,4-β-Dglucanase, endo-1,3 1,4-β-D-glucanase, β-1,3-glucanase, β-galactosidase and cellulase. Regulation of cell wall biosynthesis is complex. To build secondary cell walls, genes encoding cellulose, hemicellulose and lignin biosynthetic enzymes need to be coordinately expressed in a cell type specific manner. Secondary wall-associated NAC domain protein 1 (SND1) acts as a master switch turning on a subset of MYB and NAC transcription factors, which then activate the developmental machinery of secondary wall biosynthesis in fibers of Arabidopsis (Zhong & Ye, 2007; Zhong et al., 2008). Lignin and hemicellulose reduce the value of plant fibers for composite manufacture because, compared to cellulose, they degrade at different temperatures. Hemicellulose degrades at a lower temperature compared to cellulose while lignins with different monolignol components degrade at different temperatures. However, it has been shown that lignin compositions and amounts can be engineered to improve the processing efficiency of plant biomass (Vanholme et al., 2008). The interactions between lignin biosynthesis and other metabolic pathways as well as the cellulose and hemicellulose networks can also influence fiber traits. As pectin has roles in both primary and secondary cell walls, manipulation of pectin synthesis is expected to have diverse impacts on plant properties, including plant biomass (Mohnen, 2008). The principal aim of this work was to analyze the structure and properties of soybean stem fibers with a view to assessing their potential as additions for composites for the automotive industry. In order to simplify and accelerate future selection and/or molecular manipulation of plant fiber characteristics, the first step is to increase the information about the structural and regulatory genes that control the synthesis and modification of cell wall components (Reinprecht et al., 2010).

2. RG10 x OX948 – LLA.3lx RIL population 2.1 RIL population development and evaluation The soybean RIL population from the RG10 x OX948 cross was developed for a study to improve soybean oil and protein flavour. The population was derived from reciprocal crosses between a LLA line (RG10) and a 3lx line (OX948) and consists of 169 RILs (Reinprecht et al., 2005). Mutant line RG10, used as the source for the LLA trait, was developed at the University of Guelph, Ridgetown Campus, Ridgetown, Ontario, Canada by chemical mutation of the LLA line C1640 (an ENS mutant of cultivar Century) with EMS (an EMS mutant of cultivar Century). The allele at the Fan locus in RG10 is designated as fan-b, for a very low content (< 25 g kg-1) of LA (Stojsin et al., 1998). The 3lx mutant line OX948 was developed at Agriculture and Agri-Food Canada, Greenhouse and Processing Crops Research Centre, Harrow, Ontario, Canada (Buzzell, unpublished). OX948 is a selection from a cross between Harovinton and a 3lx F2 plant [produced by γ-irradiation; F1 seed was obtained from Dr. Kitamura (National Agricultural Research Centre, Ministry of Agriculture, Forestry and Fisheries, Tsukuba, Japan)]. The 3lx line was selected at the F4 stage and included in yield trials. Both RG10 and OX948 were evaluated for yield and other agronomic characteristics. The parental lines were lower yielding compared to adapted commercial cultivars.

A Versatile Soybean Recombinant Inbred Line Population Segregating for Low Linolenic Acid and Lipoxygenase Nulls - Molecular Characterization and Utility…

125

Reciprocal crosses between RG10 (fan-bLx1Lx2Lx3) and OX948 (Fanlx1lx2lx3) were made in the growth room at the Department of Plant Agriculture, University of Guelph. The F1 plants, segregating F2 plants and parents were evaluated under controlled indoor environments [16 h light (200 μmol m-2 s-1) and 8 h dark, 26 and 22oC, respectively] in order to reduce possible environmental effects on fatty acid content. Single seed descent was used to accelerate inbreeding. A portion of a single seed (~2/3 of the seed with embryo axis remaining after 1/3 of the seed was removed and used to determine the fatty acid composition and presence of LX) from each F2 line was planted in a growth room using a 12 h day length until flowering, followed by a 16 h day length (26oC day and 22oC night) with a light intensity of 500 μmol m-2 s-1, to produce F3 seeds. The identity of each F2 line and parent seeds was maintained during the inbreeding. The F3 seed was advanced to the F5 generation in Belize and the F5 lines were increased in single rows at Ridgetown and Harrow during the summer of 1999 (Reinprecht et al., 2006a). The RIL populations [at the F6 and F7 stages together with parents and two check cultivars (OAC Bright and OAC Glencoe)] were evaluated for a number of seed and agronomic traits at three Ontario locations (Harrow, Ridgetown and Woodslee) in 2000 and 2001. The seed and agronomic characteristics of six LLA.3lx ( IAC-15 ≥ Santa Rosa.

5. References Arru, L.; Rognoni, S.; Baroncini, M.; Bonatti, P.M. & Perata, P. (2004). Copper localization in Cannabis sativa L. grown in a cooper-rich solution. Euphytica, Vol.140, (33-38), ISSN 0014-2336. Baldisserotto, C.; Ferroni, L.; Medici, V.; Pagnoni, A.; Pellizzari, M.; Fasulo, M.P.; Fagioli, F.; Bonora, A. &Pancaldi, S. (2004). Specific intra-tissue responses to manganese in the floating lamina of Trapa natans L. Plant Biology, Vol.6, (578-589), ISSN 1435-8603. Bidwell, S.D.; Woodrow, I.E.; Batianoff, G.N. & Sommer-Knudsen, J. (2002). Hyperaccumulation of manganese in the rainforest tree Austromystus bidwillii (Myrtaceae) from Queensland, Australia. Functional Plant Biology, Vol.29, (899-905), ISSN 14454408. Broadhurst, C.L.; Chaney, R.L.; Angle, J.S.; Maugel, T.K.; Erbe, E.F. & Murphy, C.A. (2004). Simultaneous hyperaccumulation of nickel, manganese and calcium in Alyssum leaf trichomes. Environmental Science & Technology, Vol.38, (5797-5802), ISSN 1520-5851. Broadley, M.R.; White, P.J. (2005). Plant nutritional genomics. Blackwell Publishing: Oxford, UK. Demirevska-Kepova, K.; Simova-Stoilova, L.; Stoyanova, Z.; Holzer, R. & Feller, U. (2004). Biochemical changes in barley plants after excessive supply of cooper and manganese. Environmental and Experimental Botany, Vol.52, (253-266), ISSN: 00988472. Di Toppi, L.S.; Musetti, R.; Vattuone, Z.; Pawlik-Skowronska, B.; Fossati, F.; Bertoli, L.; Badiani, M. & Favali, M.A. (2005). Cadmium distribution and effects on ultrastructure and chlorophyll status in photobionts and mycobionts of Xathoria parietina. Microscopy Research and Technique, Vol.66, (229-238), ISSN (electronic) 1097-0029. Doncheva, S.; Georgieva, K.; Vassileva, V.; Stoyanova, Z.; Popov, N. & Ignatov, G. (2005). Effects of succinate on manganese toxicity in pea plants. Journal of Plant Nutrition, Vol.28, (47-62), ISSN (electronic): 1532-4087. Ducic, T. & Polle, A. (2005). Transport and detoxification of manganese and copper in plants. Brazilian Journal of Plant Physiology, Vol.17, (103-112), ISSN: 16770420.

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Epstein, E. & Bloom, A.J. (2005). Mineral nutrition of plants: principles and perspectives. Sinauer: Sunderland, MA, USA. Fageria, N.K. (2001). Adequate and toxic levels of copper and manganese in upland rice, common bean, corn, soybean and wheat grown on an oxisol. Communications in Soil Science and Plant Analysis, Vol.32, (1659-1676), ISSN: 1532-2416. Fernando, D.R.; Bakkaus, E.J.; Perrier, N.; Baker, A.J.M.; Woodrow, I.E. Batianoff, G.N. & Collins, R.N. (2006a). Manganese accumulation in the leaf mesophyll of four tree species: a PIXE/EDAX localization study. New Phytologist, Vol.171, (751-758), ISSN: 1469-8137. Fernando, D.R.; Batianoff, G.N.; Baker, A.J.M. & Woodrow, I.E. (2006b). In vivo localization of manganese in the hyperaccumulator Gossia bidwilli (Benth.) N. Snow & Guymer (Myrtaceae) by cryo-SEM/EDAX. Plant, Cell & Environment, Vol.29, (1012-1020), ISSN: 1365-3040. Fitter, A. C. (1996). Characteristics and functions of root systems. In: Waisel, Y.; Eshel, A. & Kafkafi, U., eds. Plant roots: The Ridden Half. 2. ed., p.1-20. New York: Marcel Dekker. Foy, C.D.; Scott, B.J.; & Fisher, J.A. (1988). Genetics differences in plant tolerance to manganese toxicity. In: Graham, R.D.; Hannam, R.J.; Uren, N.C., eds. Manganese in soils and plants. p.293-307. Kluwer Academic Publishers, Dordrecht, The Netherlands. Führs, H.; Hartwig, M.; Molina, L.E.B.; Heintz, D.; Van Dorsselaer, A.; Braun, H.P. & Horst, W.J. (2008). Early manganese-toxicity response in Vigna unguiculata L. – a proteomic and transcriptomic study. Proteomics, Vol.8, (149-159), ISSN: 1615-9861. González, A. & Lynch, J.P. (1999). Subcellular and tissue Mn compartmentalization in bean leaves under Mn toxicity. Australian Journal of Plant Physiology, Vol.26, (811-822), ISSN: 0310-7841. Graham, R.D. (1988). Genotypic differences in tolerance to manganese deficiency. In: Graham, R.D.; Hannam, R.J.; Uren, N.C., eds. Manganese in soils and plants. p.261276. Kluwer Academic Publishers, Dordrecht, The Netherlands. Henriques, F.S. (2003). Gas exchange, chlorophyll a fluorescence kinetics and lipid peroxidation of pecan leaves with varying manganese contents. Plant Science, Vol.165, (239-244), ISSN 0306-4484. Henriques, F.S. (2004). Reduction in chloroplast number accounts for the decrease in the photosynthetic capacity of Mn-deficient pecan leaves. Plant Science, Vol.166, (10511055), ISSN 0306-4484. Huber, D.M. & Graham, R.D. (1999). The role of nutrition in crop resistance and tolerance to diseases. In: Rengel, Z., ed. Mineral nutrition of crops: fundamental mechanisms and implications. p.169-204. New York: Food Products Press, Izaguirre-Mayoral, M.L. & Sinclair, T.R. (2005). Soybean genotypic difference in growth, nutrient accumulation and ultrastructure in response to manganese and iron supply in solution culture. Annals of Botany, Vol.96, (149-158), ISSN 1095-8290. Küpper, H.; Zhao, F.J. & Mcgrath, S.P. (1999). Cellular compartmentation of zinc in leaves of the hyperaccumulator Thlaspi caerulescens. Plant Physiology, Vol.119, (305-311), ISSN 1532-2548.

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Johnson, C.M.; Stout, P.R.; Broyer, T.C. & Carlton, A.B. (1957). Comparative chlorine requirements of different plants species. Plant and Soil, Vol.8, (337-353), ISSN: 15735036. Jungk, A.O. (1996). Dynamics of nutrient movement at the soil-root interface. In: Waisel, Y.; Eshel, A.; Kafkafi, U. (Eds.) Plant roots: the ridden half. 2.ed. New York: Marcel Dekker, p.529-556. Lavres Jr., J. (2007). Influência genotípica na absorção, utilização e na toxidez de manganês na soja. Piracicaba: University of Sao Paulo, 88p. PhD Thesis. Lavres Jr, J.; Malavolta, E.; Nogueira, N.L.; Moraes, M.F.; Reis, A.R.; Rossi, M.L. & Cabral, C.P. (2009). Changes in anatomy and root cell ultrastructure of soybean genotypes under manganese stress. Brazilian Journal of Soil Science, Vol.33, (395-403), ISSN 1806-9657. Lavres Jr, J.; Reis, A.R.; Rossi, M.L.; Cabral, C.P.; Nogueira, N.L. & Malavolta, E. (2010). Changes in the ultrastructure of three soybean cultivars in response to manganese supply in solution culture. Scientia Agricola, Vol.67, (287-294), ISSN 0103-9016. Lidon, F.C. (2002). Rice plant structural changes by addition of excess manganese. Journal of Plant Nutrition, Vol.25, (287-296), ISSN (electronic): 1532-4087. Lima, D.V.; Kliemann, H.J.; Moraes, M.F. & Leandro, W.M. (2004). Effect of liming and manganese rates on soybean mineral nutrition in the region of Rio Verde, State of Goias, Brazil. Tropical Agriculture Research, 34: 61-69. Malavolta, E.; Vitti, G.C. & Oliveira, S.A. (1997). Avaliação do estado nutricional das plantas: princípios e aplicações. 2.ed. Piracicaba: POTAFOS, 319p. Malavolta, E. (2006). Handbook of plant mineral nutrition. Editora Agronômica Ceres: São Paulo, Brazil. (In Portuguese). Morita, A.; Yokota, H.; Ishka, M.R. & Ghanati, F. (2006). Changes in peroxidase activity and lignin content of cultured tea cells in response to excess manganese. Soil Science and Plant Nutrition, Vol.52, (26-31), ISSN 0038-0768. Mcquattie, C. J. & Schier, G.A. (2000). Response of sugar maple (Acer saccharum) seedlings to manganese. Canadian Journal of Forest Research, Vol.30, (456-467), ISSN: 1208-6037. Nye, P.H. & Tinker, P.B. (1977). The uptake properties of the root system in solution. In: Nye, P.H.; Tinker, P.B. (Eds.) Solute movement in the soil-root system. 1.ed. Oxford: Blackwell Scientific Publications, p.92-126. Papadakis, I.E.; Bosabalidis, A.M.; Sotiropoulos, T.E. & Therios, I.N. (2007a). Leaf anatomy and chloroplast ultrastructure of Mn-deficient orange plants. Acta Physiologia Plantarum, Vol.29, (297-301), ISSN: 1861-1664. Papadakis, I.E.; Giannakoula, A.; Antonopoulou, C.P.; Moustakas, M.; Avramaki, E. & Therios, I.N. (2007b). Photosystem II activity of Citrus volkameriana (L.) leaves as affected by Mn nutrition and irradiance. Photosynthetica, Vol.45, (208-213), ISSN: 1573-9058. Papadakis, I.E.; Giannakoula, A.; Therios, I.N.; Bosabalidis, A.M.; Moustakas, M. & Nastou, A. (2007c). Mn-induced changes in leaf structure and chloroplast ultrastructure of Citrus volkamericana (L.) plants. Journal of Plant Physiology, Vol.164, (100-103), ISSN 0176-1617. Pittman, J.K. (2005). Managing the manganese: molecular mechanisms of manganese transport and homeostasis. New Phytologist, Vol.167, (733-742), ISSN: 1469-8137.

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Reid, R.J. (1999). Kinetics of nutrient uptake by plant cells. In: Rengel, Z. (Ed.) Mineral nutrition of crops: fundamental mechanisms and implications. 1.ed. New York: Food Products Press, p.41-66. Rengel, Z. (1999). Physiological mechanisms underlying differential nutrient efficiency of crop genotypes. In: Rengel, Z. Mineral nutrition of crops: fundamental mechanisms and implications. p.227-265. New York: Food Products Press. Rosolem, C.A.; Sacramento, L.V.S & Oliveira, D.M.T. (2005). Kinetics of zinc uptake and anatomy of roots and leaves of coffee trees as affected by zinc nutrition. Journal of Plant Nutrition, Vol.28, (2101-2112), ISSN (electronic): 1532-4087. Santandrea, G. Schiff, S. & Bennici, A. (1998a). Effects of manganese on Nicotiana species cultivated in vitro and characterization of regenerated Mn-tolerant tobacco plants. Plant Science, Vol.132, (71-82), ISSN 0306-4484. Santandrea, G. Tani, C. & Bennici, A. (1998b). Cytological and ultrastructural response of Nicotiana tabacum L. roots to manganese stress. Plant Biosystems, Vol.132, (197-206), ISSN (electronic): 1724-5575. Sas Institute. SAS/STAT. (1996). User's guide, version 6.11. Cary, North Carolina, USA. Tanaka, R.T.; Mascarenhas, H.A.A.; Bulisani, E.A. (1992). Manganese deficiency in soybean due to excessive liming. Brazilian Journal of Agricultural Research, Vol.27, (247-250), ISSN 1678-3921. Thompson, I.A. & Huber, D.M. (2007). Manganese and plant disease. In: Datnoff, L.E.; Elmer, W.H. & Huber, D.M., eds. Mineral nutrition and plant disease. p.139-153. Saint Paul: APS Press. Weiland, R.T.; Noble, R.D. & Crang, R.E. (1975). Photosynthetic and chloroplast ultrastructural consequences of manganese deficiency in soybean. American Journal of Botany, Vol.62, (501-508), ISSN 1537-2197. Yan, S.L.; Tsay, C.C. & Chen, Y.R. (2000). Isolation and characterization of phytochelatin synthase in rice seedlings. Proceedings of the National Science Council, Republic of China. Part B, Vol.24, (202-207), ISSN: 0255-6596.

Part 3 Breeding for Biotic Stress

12 Resistance to Pythium Seedling Disease in Soybean Rupe, J.C.1, Rothrock, C.S.1, Bates, G.2, Rosso, M. L.3, Avanzato, M. V.1 and Chen, P.1 1University of Arkansas, Arkansas Community College, 3Virginia Polytechnic Institute and State University United States of America 2Northwest

1. Introduction Pythium spp. form one of the most important groups of seedling pathogens affecting soybean. Seedling diseases reduce stands, compromise plant vigor of surviving plants and may make replanting fields necessary (Yang, 1999). Yield reductions due to Pythium spp. have also been documented with the use of selective fungicide seed treatments (Poag, et al., 2005). While generally associated with cool, wet conditions, stand problems due to Pythium spp. can occur over a wide range of temperatures. At least seventeen species of Pythium are pathogenic to soybean and these species have a wide range of temperature optima. For example, P. debaryanum, P. torulosum and P. ultimum, infect soybean at low temperatures (20 °C or less) (Thomson et al., 1971; Yang, 1999) and affect early planted soybean. However, P. aphanidermatum and P. myriotylum infect soybean at high temperatures (30 °C or higher) (Littrell and McCarter, 1970; McCarter and Littrell, 1970; Thomson et al., 1971) and predominate in the southern United States, or when soybeans are planted late (Yang, 1999). Besides temperature, high soil water content is another important factor in seedling disease development. Saturated soils place plants under stress increasing loss of exudates from roots, diffusion of exudates away from the roots and, with many Pythium species, resulting in the production of motile zoospores that can swim in free water in the soil. All of the factors extend the distance from which these pathogens can recognize the presence of a host and infect the plant. These conditions are common in many soybean production areas, including Arkansas soybean fields. Soybean is an important crop in the United States with more than 29 million hectares planted annually. Arkansas producers plant 1.2 to 1.4 million hectares annually with most soybean production in the state occurring on alluvial soils with poor internal and surface drainage. These soils are easily saturated favoring seedling diseases especially by Pythium spp. (Kirkpatrick et al., 2006b). In addition, soybean planting begins in April and often continues through June and into July, meaning that soybeans may experience a wide range of temperatures at planting. With the large number of Pythium spp. reported as pathogenic to soybean, there are often pathogenic species that are active at whatever soil temperatures occur at planting. The usual method of controlling seedling diseases caused by Pythium spp.

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is to use fungicide seed treatments that include either metalaxyl or the active isomer mefenoxam (McGee, 1992). Plant resistance to Pythium damping-off is rare and has not been actively pursued in breeding programs; however we have identified a cultivar with high levels of resistance to Pythium damping-off that may be useful to reducing the impact of this disease in soybean (Rosso et al., 2008). In 1996, we began investigating the effect of cultivars on stand when subjected to early flooding and which pathogens were favored by these saturated conditions. This was part of a larger project on flood tolerance supported by a grant from the United Soybean Board and headed by Dr. Tara VanToai. In this study, we compared the resulting stands of six cultivars with a range of flood tolerances subjected to no flood, flood at emergence or flood at the V4 growth stage. In addition we assayed roots to determine which pathogens were affected by flooding. Significant differences in stand among the cultivars occurred, especially with the flood at emergence. Of the pathogen groups isolated, Macrophomina phaseolina, Fusarium spp., and Pythium spp. responded to flooding with only the frequency of isolation of Pythium spp. increasing with flooding (Table 1) (Kirkpatrick et al., 2006b). In the final year of the study, the cultivar Archer was included in the cultivar comparison. M. phaseolina___ 1996 1997 1998

1996

No Flood Emergence LSDc LSDd

4.5 3.0

8.9 3.0 ns ns

11.5 10.0

10.9 20.9

16.7 27.3 18.5 20.7

9.3 55.3

7.4 10.4

67.0 62.4 17.8 20.0

84.4 53.9

No Flood V4 LSDc LSDd

56.5 29.9

54.0 24.8 8.9 10.6

34.8 24.8

8.1 42.8

22.3 31.1 8.1 14.4

10.0 45.9

51.7 37.4

49.4 56.7 12.9 13.5

71.5 56.7

Flood

Pythium spp.___ 1997 1998

Fusarium spp.__ 1996 1997 1998

aPlots were flooded for 3 days when the plants were emerging or for 7 days when the plants reached the four leaf growth stage (V4). bFifteen plants per plot of the cultivars Hartz 5164, Crowley, Asgrow 4715, NK S59-60, RVS-499, and TB881266 were sampled 4 days (1996) or 3 days (1997 and 1998) after removal of the flood and the roots were assayed for filamentous eukaryotic organisms. cLeast significant differences (LSD) to compare flood treatments within a year (P = 0.05). dLSD to compare flood treatments among years (P = 0.05).

Table 1. Effect of flooding at soybean emergence or V4a growth stage on recovery (%) of Macrophomina phaseolina, Pythium spp., and Fusarium spp. from soybean roots of six genotypesb sampled over three years (Kirkpatrick, et al. 2006b). Archer was a maturity group I cultivar that another group on the project had identified as highly flood tolerant (Lark, 2001, VanToai, et al. 2001). While significant differences between cultivars in these tests have been observed previously for flooding at emergence, relative stands of Archer were much higher than any other cultivar evaluated during the study (Table 2).

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Cultivar Archer Asgrow 4715 Crowley Hartz 5164 a b

Flood 36.7 3.3 0.0 0.0

No-flood 76.7 63.3 50.0 56.7

Stands taken four weeks after planting and are the mean of four replications. Plots flooded for 24 hours at emergence.

Table 2. Stands (%)a of cultivars when floodedb or not flooded at emergence in the field (Kirkpatrick, et al. 2006b). To determine if the greater stands in Archer than the other cultivars was due just to flood tolerance or if seedling pathogens were involved, greenhouse studies were conducted comparing Archer to the cultivar Hutcheson. Hutcheson was a commonly grown, maturity group V cultivar that was sensitive to flooding. These cultivars were either flooded for 24 hr at emergence or not flooded and the soil either infested or not infested with P. ultimum. Pythium ultimum was chosen as the seedling pathogen since Pythium spp. were the only group of pathogens that increased with flooding and P. ultimum was one of the principle species isolated in the field test. In the absence of the pathogen, stands of both cultivars were similar and did not appear to be affected by flooding (Table 3). However, stands of Hutcheson were significantly reduced in the presence of P. ultimum in the non-flooded treatment and were completely eliminated in the flooded/infested treatment. Stands of Archer, on the other hand, were not affected by the presence of P. ultimum in the nonflooded treatment and were lower in the flooded/infested treatment, but were significantly higher than those of Hutcheson in either the no flood/infested or flood/infested treatments. While not immune, Archer demonstrated a high level of resistance to P. ultimum under these conditions. Cultivar Flood + LSDb LSDc LSDd

Archer Non-infested Infested 9.83 8.83 8.83 3.67 1.54 0.99 1.19

Hutcheson Non-infested Infested 9.17 1.50 8.67 0.00

a Means represent the combined data from two experiments, with ten seeds planted per experimental unit. b LSD (least significant difference) to compare infestation treatments within the same cultivar and flood treatment (P=0.05). c LSD to compare flood treatments within the same cultivar and same or different infestation treatments (P=0.05). d LSD to compare cultivars within the same or different flood and infestation treatments (P=0.05).

Table 3. Plant stand for two soybean cultivars at seven days, after termination of flooding at emergence, when seed were planted directly into soil infested with P. ultimum or noninfested soila (Kirkpatrick, et al. 2006a).

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Besides P. ultimum, four other Pythium spp. pathogenic to soybean were isolated in the field study, P. aphanidermatum, P. irregulare, P. vexans (=Phytopythium vexans) (Bala, et al. 2010), and HS group (a group that does not produce oospores or sporangia that would allow it to be identified to species), and the non-pathogenic species P. oligandrum. The effect of these species on stand and root rot in Archer and Hutcheson were compared (Bates, et al. 2008). With each Pythium species, stands of Hutcheson were significantly lower than those of Archer (Table 4). Similarly, root rot was significantly higher with Hutcheson than with Archer with all of the species except P. oligandrum which did not cause damage on either cultivar.

Pythium spp. P. aphanidermatum

Group HS P. irregulare P. oligandrum P. ultimum P. vexans

Isolate 16 64 88 117 126 21 115 120 125 124 140 182

Archer 9.1 Add 9.2 Acd 9.2 Ad 9.4 Abc 9.4 Abc 9.5 Ab 9.3 Acd 10.0 Aa 10.0 Aa 9.5 Ab 9.4 Abc 9.1 Ad

Plant standb Hutcheson 5.0 Bf 5.0 Bf 5.2 Bf 6.1 Bd 6.4 Bc 3.6 Bg 3.6 Bg 9.0 Ba 8.8 Ba 7.0 Bb 5.6 Be 5.9 Bd

Disease ratingc Archer Hutcheson 0.7 Bef 2.9 Acd 0.6 Bf 3.0 Acd 0.4 Bf 2.9 Acd 2.0 Ba 3.2 Ac 1.8 Bab 3.1 Acd 1.6 Bbc 4.8 Aa 1.6 Bbc 4.8 Aa 0.0 Ag 0.0 Ae 0.0 Ag 0.0 Ae 1.0 Bde 2.8 Ad 1.1 Bd 4.6 Aab 1.2 Bcd 4.4 Ab

a Seed were placed in direct contact with an inoculum layer of Pythium isolates in pots and grown for 7 days at 20°C. Means represent five combined experiments with five replications each. bPlant stand from 10 seeds planted. cDisease rating based on scale of 0 to 10, where 0 = healthy and 1 = 1 to 10, 2 = 11 to 20, 3 = 21 to 30, 4 = 31 to 40, 5 = 41 to 50, 6 = 51 to 60, 7 = 61 to 70, 8 = 71 to 80, 9 = 81 to 90, and 10 = 91 to 100% root discoloration, and analyzed as the mid-percentile value. dCultivars for a Pythium isolate did not significantly differ if followed by the same capital letter, protected least significant difference (LSD; P = 0.05). Within a cultivar, Pythium isolates did not differ significantly if followed by the same lower-case letter, protected LSD (P=0.05).

Table 4. Plant stand and disease rating of Archer and Hutcheson for the emergence assaya (Bates et al. 2008). There appears to be two phases to seedling disease caused by Pythium spp., a seed rot and a root rot phase, and the resistance in Archer affects both phases. Placing seed directly on P. ultimum inoculum severely reduced stands of Hutcheson in non-flooded soils and there was no stand in flooded soils. (Kirkpatrick et al. 2006a). With Archer, placing seed directly on the inoculum did not affect stand unless the plots were flooded and then stands were reduced by almost 65%. Under these experimental conditions, most of the seedling disease was probably due to seed rot. In order to determine the effect of P. ultimum on root rot, Kirkpatrick et al. (2006a) separated the seed from the inoculum with 2 to 5 mm of sterile soil (Table 5). This thin layer of sterile soil allowed the seeds to germinate before contacting the pathogen. When this was done, most seeds of both cultivars emerged. Pythium ultimum significantly increased root rot over the non-infested treatments only in flooded soil. This increase was significant for both cultivars, but was significantly greater for Hutcheson than for Archer. The reduction in stand and the degree of root rot was also affected by seed

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P. ultimum Non-infested Infested

Archer No flood Flood 6.13 16.73 9.33 32.93 LSDc= 9.09 LSDd= 8.21 LSDe=11.25

Hutcheson No flood Flood 5.37 6.33 11.07 49.50

a Root rating scale from 0 to 5; with 0 =no discoloration, 1=1-10%, 2=11-25%, 3=26-50%, 4=51-75%, and 5 =76-100% discoloration, and analyzed as the mid-percentile value. b Data represent combined over two experiments. c LSD (least significant difference) to compare infestation treatments within same flood treatment (P=0.05). d LSD to compare infestation treatments within the same cultivar and flood treatments (P=0.05). e LSD to compare flood treatments within the same cultivar and same or different infestation treatments (P=0.05).

Table 5. The effect of Pythium ultimum and flooding at soybean emergence on root discolorationa for the two soybean cultivars Archer and Hutcheson when planted on a layer of pasteurized soil over the soil treatmentb (Kirkpatrick, et al. 2004a) quality. Nanayakkara (2001) using this inoculum layer technique with four Pythium species found that stands of Hutcheson were significantly lower with the lowest seed quality, but root rot increased significantly with the medium seed quality across all Pythium species (Table 6). _______________________________________________________________________ Archer Hutcheson Pythim spp. High Medium Low High Medium Low Stand (%) Non-infested 93.3 86.7 80.0 100.0 93.4 93.3 P. vexans 86.7 73.3 53.3 100.0 93.3 73.3 P. aphanidermatum 86.7 66.7 20.0 93.3 100.0 66.7 P. ultimum 100.0 100.0 26.7 100.0 86.7 100.0 P. irregular 86.7 26.7 20.0 100.0 86.7 86.7 LSD0.05=21.8 Root Rot (%) Non-infested 0.3 P. vexans 42.3 P. aphanidermatum 6.7 P. ultimum 10.7 P. irregulare 6.7 LSD0.05=15.0

2.3 53.7 32.7 18.0 22.3

3.0 81.7 50.5 75.5 63.0

0.6 8.0 3.7 7.3 5.0

1.7 15.0 6.7 10.7 10.0

1.7 54.7 10.7 31.3 22.3

aHigh quality seed of each cultivar was artificially aged to create three seed quality levels: high, medium, and low. Standard germination for Archer were 88, 77,58% and for Hutcheson were 89, 78, and 58%, respectively. b Vermiculite was used as the potting medium. Seed was separated from a layer of sand/corn meal inoculum by 1.5 cm of sterile vermiculite.

Table 6. The effect of Pythium spp. on stand and root rot of high, medium, and low quality seedlotsa of the cultivars Archer and Hutcheson at 20 0Cb (Nanayakkara, 2001).

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Pythium spp.

Isolate

Root discolorationb Archer Hutcheson

P. aphanidermatum

16 64 88 117 126 21 115 120 125 124 140 182

0.0 Bcc 0.1 Bc 0.0 Bc 0.0 Bc 0.0 Bc 1.6 Ba 1.5 Ba 0.0 Ac 0.0 Ac 1.0 Bb 0.9 Bb 0.9 Bb

Group HS P. irregulare P. oligandrum P. ultimum P. vexans

4.5 Ac 4.3 Ac 4.1 Ac 5.8 Ab 6.0 Ab 4.6 Ac 4.2 Ac 0.0 Ae 0.0 Ae 3.2 Ad 6.1 Aab 6.6 Aa

Fresh root weight (g) Archer Hutcheson 4.6 Ac 4.5 Ac 4.5 Ac 9.2 Ab 8.9 Ab 4.1 Ac 4.0 Ac 20.8 Ba 20.6 Ba 2.2 Ac 21.8 Aa 21.4 Aa

3.0 Bb 3.1 Ab 3.1 Ab 2.6 Bb 2.4 Bbc 2.3 Bbc 2.4 Bbc 40.9 Aa 40.1 Aa 1.6 Bc 2.4 Bbc 2.4 Bbc

a Inoculum was separated from seeds in pots by a layer of pasteurized soil. The experiment was terminated after 6 weeks. Means represent two combined experiments with five replications. Each pot was thinned to five plants. b Disease rating based on scale of 0 to 10, where 0 = healthy and 1 = 1 to 10, 2 = 11 to 20, 3 = 21 to 30, 4 = 31 to 40, 5 = 41 to 50, 6 = 51 to 60, 7 = 61 to 70, 8 = 71 to 80, 9 = 81 to 90, and 10 = 91 to 100% root discoloration, and analyzed as the mid-percentile value. cCultivars for a Pythium isolate did not differ significantly if followed by the same capital letter, protected least significant difference (LSD; P = 0.05). Within a cultivar, Pythium isolates did not differ significantly if followed by the same lower-case letter, protected LSD (P = 0.05).

Table 7. Root discoloration and fresh root weight of Archer and Hutcheson for the plant growth assaya(Bates et al. 2008). Significant reductions in stand and increases in root rot only occurred at the lowest quality seed lot with Archer and not with all species. With low quality seed lots, Archer had significantly greater stands and less root rot than Hutcheson. Bates et al. (2008) found that root rot was lower and root weights were higher for Archer than Hutcheson when inoculated with P. aphanidermatum, HS group, P. irregulare, P. ultimum, or P. vexans. Reductions in root weights were observed in Hutcheson after two weeks, but not in Archer. After six weeks, reductions in root weights occurred in both cultivars but were greater for Hutcheson than Archer (Table 7). Root weights were not reduced with Archer after six weeks when inoculated with P. vexans, but were sharply reduced in Hutcheson with this species. To determine if this resistance in Archer was effective in the field, Archer and Hutcheson were treated or not treated with metalaxyl and planted at five locations in Arkansas at three planting dates (April, May, and June) and flooded or not flooded at emergence. Stands were taken four weeks after planting. The test was conducted for three years and resulted in 140 comparisons. To determine the effectiveness of Archer resistance, the number of tests in which the metalaxyl treatment had a significantly greater stand than the non-treated control for each cultivar was determined. There were significantly more tests where Hutcheson responded to metalaxyl (41 tests) than did Archer (18 tests) showing that the resistance in Archer was reducing the impact of Pythium seedling disease under field conditions (unpublished data). In addition, the effect of metalaxyl occurred at all planting dates, with both seed qualities, and in flooded and non-flooded treatments clearly showing that

267

Resistance to Pythium Seedling Disease in Soybean

seedling diseases caused by Pythium spp. occurred across a wide range of environmental conditions and seed qualities. The cultivar Archer was developed in Iowa and released in 1990, because of its resistance to specific races of Phytophthora sojae (Cianzio et al., 1991). ‘Archer’ has the resistance genes Rps6 and Rps1k, derived from ‘PRX54-59’ and ’Williams 82’, respectively. Previous studies speculated that Archer resistance to Pythium could be associated with Phytophthora resistance genes (Rps), particularly to Rps1k (Bates et al., 2004). Bates et al. (2004) observed that when a set of differential cultivars containing specific resistance genes for P. sojae were planted in vermiculite infested with P. aphanidermatum and assessed for disease, the cultivar Williams 82 (Rps 1k) demonstrated resistance to P. aphanidermatum similar to Archer resistance. In addition, Pioneer (Pioneer Seed Co., Johnston, Iowa) cultivars 94M70 and 94M41 (with Rps1k) inoculated in the greenhouse with P. aphanidermatum using a hypocotyl inoculation technique showed significantly higher plant survival than cultivars 94B13 and 94M90 (without Rps1k) (Rosso et al., 2005). Pioneer cultivars with the Rps1k gene also had significantly less root discoloration and Pythium incidence in field trials than those without the gene (Rosso et al., 2005). However, the interpretation of the role of Rps 1k in resistance was complicated since Williams 82 was not only a parent of Archer, but in the background of other lines having Rps 1k and thus other genes from this parent.

A†

B

A. Resistant reaction in the resistant parent ‘Archer’. B. Susceptible reaction in the susceptible parent ‘Hutcheson’. †

Fig. 1. Reactions of ‘Archer’ and ‘Hutcheson’ plants seven days after inoculation with P. aphanidermatum by the hypocotyl inoculation technique (Rosso et al., 2008).

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Chi-square 1:2:1 ratio (df=2)

No. of Plantsb Parents/ populationa

R

‘Archer’ (R)

34

‘Hutcheson’ (S)

2

‘Archer’ x ‘Hutcheson’

21

I

S

Value

P-value

1.53

0.5-0.25

6 38 48

17

aEach experimental unit contained 10 plants and was repeated 4 times. Experiment performed under 12h day length at 28 ºC. The experiment was run twice. bR= resistant (> 70% plant survival); I= intermediate reaction (69 to 31 %, plant survival); S= susceptible (< 30% plant survival).

Table 8. Reactions to Pythium damping-off of parents and F2:4 lines from an ‘Archer’ (R) x ‘Hutcheson’ (S) cross in hypocotyl inoculationsa with Pythium aphanidermatum . (Rosso et al., 2008). Rosso et al. (2008) made a cross between Archer and Hutcheson, and 86 F2:4 lines were developed. Resistance in these lines as well as the parents was tested by using a hypocotyl inoculation method with P. aphanidermatum. The hypocotyl inoculation method was developed for identifying major resistance genes to Phytophthora sojae (Dorrance, et al. 2004; Gordon, et al. 2007). The results showed a clear separation between the parents by phenotype. When plants were inoculated in the hypocotyl with P. aphanidermatum nearly all seedlings were killed in the susceptible parent Hutcheson whereas most of the plants remained healthy in the resistant parent Archer (Fig. 1). Using this method to screen the 86 F2:4 lines, resulted in a ratio of resistant:segregating:susceptible lines that fit a 1:2:1 ratio consistent with a single gene for resistance (Table 8). These lines were then screened with race 7 of Phytophthora sojae to determine which lines carried the Rps1k gene. These results also indicated a single gene as expected, but linkage analysis indicated that resistance to Pythium was regulated by a separate gene, not linked to Rps1k. The Pythium resistance gene was designated Rpa1. Resistance to Pythium spp, conferred by a single dominant gene has been also reported in other crops. Yang et al. (2005) found that resistance to P. inflatum, the causal agent of stalk rot in maize, was controlled by a single dominant gene, (Rpi1) in the cross of the maize inbred lines 1145 (resistant) and Y331 (susceptible). In periwinkle (Catharanthus roseus), resistance to dieback, caused by P. aphanidermatum was reported to be governed by a single gene and inherited independently of genes governing dwarfness and stem pigmentation (Kulkarni and Baskaran, 2003). Otsyula et al. (2003) indicated that the nature of resistance to P. ultimum root rot, in the bean genotypes, MLB 49-89A and 1062 and RWR 719 is controlled by a single dominant gene. Likewise, Mahuku et al. (2005) reported that a single dominant gene conditioned resistance to P.ultimum var. ultimum in Phaseolus vulgaris. The location of Rpa1 was determined using 88 SSR primers from the 20 major linkage groups (MLG) in soybean (Rosso, et al. 2008). These primers were tested against the parents, Archer and Hutcheson, and a resistant bulk made up of 12 of the resistant F2:4 lines and a susceptible bulk composed of 10 of the susceptible F2:4 lines. Two markers, Satt114 and Satt 510, were polymorphic between the parents and the resistant and susceptible bulks. Evaluating the 86 F2:4 lines, it was found that some lines had the same bands as Archer, or Hutcheson or both (Fig. 2). In the inoculation tests, these lines were classified as resistant,

Resistance to Pythium Seedling Disease in Soybean

269

susceptible, or segregating, respectively. Satt114 and Satt510 were 15.5 cM and 10.6 cM from Rpa1, respectively, and were located on the MLG F of the soybean genome (Fig. 3). Besides Rpa1, MLG F contains many other single-gene disease resistance loci and QTL’s. For example, there are two resistance genes that confer resistance to P. sojae (Rps), Rps3 and (Burnham et al., 2003a, Demirbas et al., 2001, Sandhu et al. 2005), one resistance gene that confers resistance to Phomopsis seed decay (Rpsd1) (Jackson, et al. 2005). The linkage group F also contains additional disease resistance loci conferring bacterial disease resistance (Pseudomonas syringae pv. glycinea) and two viral resistance alleles, Rsv1 (resistance to Soybean mosaic virus) and Rpv1 (resistance to Peanut mottle virus) (Ashfield et al., 1998; Ashfield et al., 2004; Gore, 2000; Hayes et al., 2004; Koning et al., 2002; Schmittehener, 1999). A QTL for partial resistance to P. sojae was reported on MLG F between marker Satt252 and Satt423 (Burnham et al., 2003b) and QTL’s conferring resistance to Meloidogyne javanica, M. arenaria, Heterodera glycines, and soybean sudden death syndrome (SDS) have been mapped on the MLG F (Concibido et al., 1994; Hnetkovsky et al., 1996; Tamulonis, et al., 1997a; Tamulonis et al., 1997b; Webb et al., 1995). Nutrient availability is a limiting factor for microbial growth and activity in agricultural soils, as a result plant pathogenic fungi and oomycetes propagules survive in soils in a state of exogenous dormancy or fungistasis (Lockwood, 1977). In order for a successful hostpathogen interaction to be initiated, fungistasis must be overcome by external stimulants (Curl and Truelove, 1986; Mitchell, 1976). Soluble and volatile exudates from germinating seeds and developing roots are the primary stimuli for such responses. Some compounds inhibit pathogen growth, thus preventing seed/root infection (Rose et al., 2006), while others have a direct beneficial effects on germination itself (Barbour et al., 1991). The primary groups of compounds released from seed and root exudates are soluble sugars, amino acids, organic acids, flavonoids, sterols and proteins (Casey, et al., 1998: Nelson, 1990; Terras, et al. 1995). Soilborne Pythium species are among the most responsive group to germinating seeds and developing roots in diverse host systems. For example, sporangia of P. ultimum and oospores of P. aphanidermatum germinate within 1.5 to 3 h after exposure to bean or pea exudates (Lifshitz et al., 1986; Stanghellini and Burr, 1973; Stanghellini and Hancock, 1971a; 1971b). Increased germination of encysted zoospores of P. aphanidermatum was observed by the addition of three-week-old pea root exudates (Chang-Ho, 1970). Exudates collected from 6-day-old red pine seedlings stimulate the germination of sporangia and promote growth of P. ultimum and P.irregulare (Agnihotri and Vaartaja, 1967a, 1970). The stimulatory effect of plant exudates on Pythium was earlier described by Agnihotri an Vaartaja (1967a; 1967b; 1970) who found mixtures of sugars and amino acids to be stimulatory to P. ultimum and P. irregulare while Chang-Ho (1970) found mixtures of glucose and organic acids to be most stimulatory to zoospore cysts of P. aphanidermatum. Furthermore, both Flentje and Saksena (1964) and Keeling (1974) observed more seed rot caused by Pythium spp. among pea and soybean cultivars that released more sugars and amino acids during germination than among those cultivars that release less. Mathew and Bradnock (1968), also observed a direct correlation between carbohydrates exudation in vitro and decreased emergence of pea seedlings due to Pythium seed rot. When seed exudates were examined, total sugar and organic acid concentrations were less in seed exudates of the Pythium resistant cultivar Archer than those in the susceptible cultivar Hutcheson (Table 9)(Nanayakara, 2001). The levels of these exudates increased as the seed quality of each cultivar was reduced, but those increases were greater with

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Soybean - Molecular Aspects of Breeding

A

L 1 2 3

1

2 L 1

4

5

6 7 8

9 10 11 12 13 14 15 16 17 18 L

3

4

2 3 4

5

5

6

7

8

6 7 8 9 L

B

A – L, 100 bp DNA ladder. Lane 1, resistant parent (‘Archer’); lane 2, susceptible parent; lanes 3-10, resistant plants; lanes 11-18, susceptible plants. B- L, 100 bp DNA ladder. Lane 1, resistant parent (‘Archer’); lane 2, susceptible parent; lanes 3-9, heterozygous plants.

Fig. 2. PCR amplification of SSR marker Satt 510 linked to Pythium damping-off (caused by P. aphanidermatum) resistance in ‘Archer’ x ‘Hutcheson’ F2:4 populations(Rosso et al., 2008).

Seed qualitya High Medium Low

Total Organic Acids Archer Hutcheson 16.97 79.03 68.69 349.07 115.40 380.32

Archer 292.89 326.24 422.63

Total Sugars Hutcheson 405.69 1,229.29 1,433.67

aHigh quality seed of each cultivar was artificially aged to create three seed quality levels: high, medium, and low. Standard germination for Archer were 88, 77, and 58% and for Hutcheson were 89, 78, and 58%, respectively.

Table 9. Concentrations (μg/seed/72hr) of total sugars and organic acids in seed exudates of Archer and Hutcheson at three levels of seed quality (Nanayakara, 2001).

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Resistance to Pythium Seedling Disease in Soybean

MLG F Satt 114

15.5 cM

‡

Rps3

†

Satt 510

10.6 cM

‡

Rpa1

‡ †

Distance in cM from the SSR markers to the resistance gene Rpa1. Rps3: Resistance gene to Phytophthora sojae (Gordon et al., 2007).

Fig. 3. Proposed genetic linkage map of Pythium damping-off (caused by Pythium aphanidermatum) resistance gene in soybean. Rpa1 is a proposed designation for the resistance gene according to the standard nomenclature for plant disease resistance genes (Rosso et al., 2008). Hutcheson than Archer. Fewer zoospores of P. aphanidermatum were detected in preference assays for seed exudates from Archer than exudates from Hutcheson seed. However, evidence suggests this response may be the result of inhibitory compounds rather than stimulatory compounds. Radial hyphal growth of Pythium isolates was inhibited by exudates from Archer seed compared to no exudates or seed exudates from Hutcheson. In addition, an ‘exchange exudates’ experiment treating imbibed Archer seed with Hutcheson exudates reduced plant stands in infested soil compared to Archer seed treated with Archer exudates while treating imbibed Hutcheson seed with Archer exudates increased plant

272

Soybean - Molecular Aspects of Breeding

stands compared to Hutcheson seed treated with Hutcheson exudates (unpublished data). Current research is being conducted to determine the major components of seed and seed exudates of Archer and Hutcheson cultivars and their roles in the resistant/susceptible responses. Differences in seed exudates do not appear to be the full story for the resistance in Archer to Pythium spp. Resistance in Archer was expressed in surviving plants in soil infestation experiment and more importantly using hypocotyls inoculations where mycelium of the pathogen is placed directly into the hypocotyls of seedling. It is unlikely that seed exudates affect the infection process under these conditions and an induced defensive response is likely. The nature of this response is not known at this time.

2. Conclusions The cultivar Archer possesses a resistance gene, Rpa1 that is located on MLG F which confers a high level of resistance to Pythium aphnidermatum and possible a number of other Pythium spp. pathogenic to soybean. This resistance is expressed under field conditions and should be useful in reducing the risk of stand loss due to this group of pathogens. In addition to protecting the plants during stand establishment where seed treatment fungicides are effective, resistance may continue throughout the season. Resistance appears to be due both to differences in pre-existent defense barriers due to compounds in seed exudates and to the induction of active defense pathways in the plant. Future work will try to better understand how these defense pathways work and if they are conferred by the same gene.

3. References Agnihotri, V.P., and Vaartaja, O. 1967a. Root exudates from red pine seedlings and their effects on Pythium ultimum. Can. J. Bot. 45:1031-1040 Agnihotri, V.P., and Vaartaja, O. 1967b. Effects on amendments, soil moisture contents, and temperature on germination of Pythium sporangia under the influence of soil mycostasis. Phytopathology 57:1116-1120 Agnihotri, V.P., and Vaartaja, O. 1970. Effect of seed exudates from Pinus resinosa and their effects on growth and germination of Pythium afertile. Plant and Soil 32:246-249 Ashfield, T., Danzer, J.R., Held, D., Clayton, K., Keim, P., Sanghai Maroof, M. A., Webb,D.M., and Innes, R.W. 1998. Rpg1, a soybean gene effective against races of bacterial blight, maps to a cluster of previously identified disease resistance genes. Theor. Appl. Genet. 96: 1013-1021 Ashfield, T., Ong, L. E., Nobuta, K., Schneider, C.M., and Innes, R.W. 2004. Convergent evolution of diseased resistance gene specificity in two flowering plant families. Plant Cell 16: 309-318 Bala, K., Robideau, G.P., Lévesque, A, Cock de, A.W.A.M., Abad, Z.G., Lodhi, A.M., Shahzad, S., Ghaffar, A., Coffey, M.D., 2010. Phytopythium Abad, de Cock, Bala, Robideau & Lévesque, gen. nov. and Phytopythium sindhum Lodhi, Shahzad & Levesque, sp. nov. Persoonia 24: 136-137 Barbour, W.M., Hatterman, D.R., and Stacey, G. 1991.Chemotaxis of Bradyrhizobium japonicum to soybean exudates. Appl. Environ. Microbiol. 57:2625-2639

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Rose, T.L., da Silva Conceição,A., Xavier-Filho, J., Okorokov, L.A., Fernandes, K.V.S., Marty, F., Marty-Mazars, D., Carvalho, A.O., and Gomes, V.M. 2006. Defense proteins from Vigna unguiculata seed exudates: characterization and inhibitory activity against Fusarium oxysporum. Plant Soil 286:181-191 Rosso, M.L., Rupe, J.C., and Rothrock, C. 2005. Resistance of soybean cultivars to Pythium damping-off and root rot based on Rps1k gene. Phytopathology 95:S90 Rosso, M.L., Rupe, J.C., Chen, P., Mozzoni, L.A. 2008. Inheritance and Genetic Mapping of Resistance to Pythium Damping-off Caused by Pythium aphanidermatum in ‘Archer’ Soybean. Crop Sc. 48 : 2215-2222 Sandhu, D., Schallock, K.G., Rivera-velez, N., Lundeen, P., Cianzio, S., and Bhattacharyya, M.K. 2005. Soybean Phytophthora resistance gene Rps8 maps closely to the Rps3 region. J. Hered. 96: 536-541 Schmitthenner, A.F.1999. Phytophthora rot. Pages 39-42 in: Compendium of soybean diseases. G.L. Hartman, J.B. Sinclair, and J.C. Rupe, ed. 4th edition. APS Press. St. Paul. MN 55121. Stanghellini, M.E., and Burr, T.J. 1973. Germination in vivo of Pythium aphanidermatum oospores and sporangia. Phytopathology 63:1493-1496 Stanghellini, M.E., and Hancock, J.G. 1971a. The sporangium of Pythium ultimum as a survival structure in soil. Phytopathology 61:157-164 Stanghellini, M.E., and Hancock, J.G. 1971b. Radial extent of the bean spermosphere and its relation to the behavior of Pythium ultimum. Phytopathology 61:165-168 Tamulonis, J.P., Luzzi, B.M., Hussey, R.S., Parrott, W.A., and Boerma, H.R. 1997a. DNA markers associated with resistance to Javanese root-knot nematode in soybean. Crop Sci. 37: 783-788 Tamulonis, J.P., Luzzi, B.M., Hussey, R.S., Parrott, W.A., and Boerma, H.R. 1997b. DNA marker analysis of loci conferring resistance to peanut root-knot nematode in soybean. Theor. Appl. Genet. 95: 664-670 Terras, F.G.R., Eggermont, K., Kovaleva, V., Raikhel, N.V., Osborn, R.W., Kester, A., Rees,B., Torrekens, S., Van Leuven, F., Vanderleyden, J., Cammue B.P.A., and Broekaert, W.F. 1995. Small cysteine-rich antifungal proteins from radish: their role in host defense. Plant Cell 7:573-588 Thomson, T.B., Athow, K.L. and Laviolette, F.A. 1971. The effect of temperature on the pathogenicity of Pythium aphanidermatum, P. debaryanum, and P. ultimum on soybean. Phytopathology 61: 933-935 VanToai, T., St. Martin, S., Chase, K., Boru, G., Schnipke, V., Schmitthenner, A. F., and Lark. K. 2001. Identification of a QTL Associated with tolerance of soybean to soil waterlogging. Crop Sci. 41:1247-1252 Webb, D.M., Baltazar, B.M. Rao-Arelli, A.P., Schupp, J., Clayton, K. Keim, P., and Beavis, W.D. 1995. Genetic mapping of soybean cyst nematode race-3 resistance loci in the soybean PI 437.695. Theor. Appl. Genet. 91: 574-581 Yang, D.E., Jin, D.M., Wang, B., Zhang, D.S., Nguyen, H.T., Zhang, C.L., and Chen, S.J. 2005. Characterization and mapping of Rpi1, a gene that confers

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dominant resistance to stalk rot in maize. Molecular Genetics and Genomics 274 (3): 229-234 Yang, X.B. 1999. Pythium damping-off and root rot. Pages 42-44 in: Compendium of Soybean Diseases. G.L. Hartman, J.B. Sinclair, and J.C. Rupe, ed. 4th edition. APS Press. St. Paul. MN 55121

13 Phomopsis Seed Decay of Soybean Shuxian Li United States Department of Agriculture-Agricultural Research Service Crop Genetics Research Unit, Stoneville, MS 38776 USA 1. Introduction Phomopsis seed decay (PSD) of soybean, Glycine max (L.) Merrill, is the major cause of poor seed quality in most soybean-growing countries (Sinclair, 1993). The disease is caused primarily by the fungal pathogen, Phomopsis longicolla, along with other Phomopsis and Diaporthe spp. PSD severely affects soybean seed quality due to reduction in seed viability and oil content, alteration of seed composition, and increased frequencies of moldy and/or split beans (Hepperly & Sinclair, 1978; Rupe and Ferriss, 1986; Rupe 1990; Wrather at al., 2004). Hot and humid environmental conditions, especially during the period from the pod fill through harvest stages, favor pathogen growth and disease development (Balducchi & McGee, 1987; Hartman et al., 1999). PSD has resulted in significant economic losses (Baird et al., 2001; Hepperly and Sinclair, 1978). Losses on a worldwide basis were approximately 0.19 million metric tons (MMT) in 1994 (Kulik & Sinclair, 1999). Effects of PSD on yields in the United States from 1996 to 2007 ranged from 0.38 to 0.43 MMT (Wrather & Koenning, 2009). In 2009, due to the prevalence of hot and humid environmental conditions from pod fill to harvest in the southern United States, PSD caused over 12 million bushels of yield losses in 16 states (Koenning, 2010).

2. Disease symptoms Soybean seeds infected by P. longicolla or other Phomopsis spp. range from symptomless to shriveled, elongated, or cracked, and often appear chalky-white (Fig. 1). Infected seeds either fail to germinate or germinate more slowly than healthy seeds. Seed infection causes pre- and post-emergence damping-off, and under severe conditions, stands can be reduced to the point of lowering yield (Kulik & Sinclair, 1999; Sinclair, 1993). Soybean pods can be infected at any time after they form. The fungi initially colonize seed coats, followed by colonization of the cotyledons and plumules. Mycelia invade the ovule and developing seeds through the funiculus and hilum. Within the seed, the fungi colonize all tissues of the seed coat and cotyledons, and eventually the radicle and plumule as well. (Kulik & Sinclair, 1999). Although P. longicolla is primarily known as a seedborne pathogen, it can be isolated from all parts of plant. It was the predominant species isolated from diseased plants collected from nine locations over a 3-year period in Canada (Xue et al., 2007). In another study, P. longicolla was the most frequently isolated fungal pathogen from both discolored and nondiscolored mature soybean stems (Harrington et al., 2000). It was also reported that P. longicolla was the major fungal species with the highest isolation frequency from all

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Fig. 1. Phomopsis seed decay caused primarily by the fungus Phomopsis longicolla. vegetative plant parts, pods, and seeds in hot and humid environments over a 3-year period (Mengistu et al., 2009).

3. The causal agents Species of the coelomycete genus Phomopsis (Sacc.) Bubák have been frequently isolated from different parts of host plants including seeds, stems, leaves and roots from a wide range of angiosperms, gymnosperms, and occasionally from bryophytes and pteridophytes (Farr et al., 1989). Over 65 species of Phomopsis have been reported as plant pathogens, and some of them cause severe diseases in economically important crops (Rehner and Uecker, 1994). Although PSD of soybean is caused primarily by Phomopsis longicolla T.W. Hobbs (Fig. 2), other Diaporthe and Phomopsis spp. have been found to be associated with the disease (Hartman et al., 1999; Sinclair, 1993).

3.1 Diaporthe – Phomopsis complex The members of the Diaporthe – Phomopsis complex consist of P. longicolla and three varieties of Diaporthe phaseolorum (Cooke and Ellis) Sacc. (anamorph P. phaseoli (Desmaz) Sacc.), in which D. phaseolorum var. caulivora K. L. Athow and R. M. Caldwell, and D. phaseolorum var. meridionalis Fernández cause stem canker of soybean while D. phaseolorum var. sojae (S. G. Lehman) Wehmeyer causes pod and stem blight (Sinclair, 1993; 1999). The Diaporthe –

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Fig. 2. Isolates of Phomopsis longicolla collected from Mississippi Delta in 2006 and grown on acidified potato dextrose agar (pH 4.5) at 24°C for 20 days. Phomopsis complex is distributed worldwide and causes more losses in soybean than any other single fungal pathogen (Sinclair, 1993). P. longicolla differs from others in the Diaporthe – Phomopsis complex in its morphology. It also does not have a known teleomorph (Hobbs et al., 1985). Although other Diaporthe and Phomopsis spp. may be associated with PSD, the disease is primarily caused by P. longicolla (Sinclair, 1993). 3.2 Discovery and description of Phomopsis longicolla Phomopsis longicolla was first identified in 1985 (Hobbs et al., 1985). The majority of isolates used in that study were obtained as mycelial cultures from soybean seed, pod, and stem tissues collected from various locations in several states in the U. S. Hobbs et al. (1985) found that isolates used in their study could be divided into two groups based on colony appearance on potato dextrose agar (acidified to pH 4.5 with 85% lactic acid) after being incubated under intermittent fluorescent light with a photoperiod of 12 hours daily for 2 weeks. Isolates from the first group clearly fit Lehman’s (1923) description of P. Sojae and were always associated with a telemorph D. phaseolorum var. sojae. In contrast, isolates from the second group never formed perithecia. These isolates differed from Lehman’s description of P. sojae, and from the isolates in the first group, in the shape of the pycnidial locule, the morphology of the conidiomata, the size and shape of the stromata, and in the conidiophore morphology (Hobbs et al., 1985). Hobbs et al. (1985) pointed out that isolates similar to the second group might have been observed in the past, but were probably reported as D. phaseolorum var. sojae. In addition, isolates in the second group were distinct from P. glycines and P. phaseoli Petch, the other Phomopsis species described from soybean. Based on the mophological differences and the pathological and ecological notes in reported

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studies (Kmetz et al., 1974; 1979), Hobbs et al. (1985) proposed that isolates similar to those in the first group retained the name of Phomopsis sojae, while isolates from the other group was described as a new species, Phomopsis longicolla. The description of P. longicolla by Hobbs et al. (1985) is as follows: “Phomopsis longicolla Hobbs, sp. Nov. Colonies on potato dextrose agar floccose, dense, white with occasional greenish-yellow areas; reverse colorless with large black stromata. Conidiomata pycnidial, black, stomatic, solitary or aggregated, unilocular or multilocular, with prominent necks more than 200 µm long, opening by apical ostioles. Locules uniostiolate or multiostiolate, globose, up to 500 µm wide. Conidiophores hyaline, simple or usually branched, septate, 3.5-24 x 1-4 µm. Conidiogenous cells hyaline, filiform, phialidic. Alpha-conidia hyaline, ellipsoid to fusiform, guttulate, 5-9.5 x 1.5-3.5 µm. Beta-conidia rare, hyaline, filiform, hamate. Isolated from seeds, pods, and stems of Glycine max (L.) Merr.; TWH P74 (BPI), Holotype, Hobbs, Wooster, Ohio, 13. XI.. 1983.” 3.3 Molecular detection and identification of the pathogens Because of the economic impacts of the PSD, accurate detection and identification of the PSD-causing pathogens are important for the development of the disease control strategies. The potato dextrose agar (PDA) seed-plating bioassay (Sinclair, 1982) is a common method used to identify causal fungi based on culture morphology. However, this traditional approach is very time-consuming and labor-intensive. Furthermore, the circumscription and identification of Phomopsis species using morphological and cultural characteristics is hampered by the plasticity of the characteristics. It was reported that in Phomopsis, morphological and cultural variation within single isolate may be as great as variation observed among isolates (Nitimargi, 1935; Parmeter, 1958). Serological assays, such as enzyme-linked immunosorbent assay (ELISA), also have also been used to detect PSD causal pathogens in plants and seeds (Brill et al., 1994; Gleason et al., 1987; and Velicheti et al., 1993). Due to insufficient specificity and sensitivity, the serological approaches are not used routinely in seed-testing laboratories. Molecular approaches, such as the polymerase chain reaction (PCR) and DNA sequencing are now commonly used to detect and identify fungal pathogens in plant tissues including soybean pathogens in soybean (Harrington et al., 2000; Li & Hartman, 2003; Zhang et al., 1997). Zhang et al. (1997) designed primers based on the sequences of nuclear ribosomal DNA that amplified the internal transcribed spacer (ITS) region of D. phaseolorum and P. longicolla. These primers could be used to specifically distinguish PSD-causal pathogens from each other and from other soybean fungal pathogens (Zhang et al., 1997). There were no differences in ITS sequences among seven geographically diverse isolates of P. longicolla (Zhang et al., 1998). Results from analysis of DNA from the Diaporthe - Phomopsis complex using PCR- restriction fragment length polymorphisms (PCR-RFLP) and DNA sequencing in the ITS and the 5.8 ribosomal DNA, along with morphological examination, indicated that P. longicolla is a distinct species, that D. phaseolorum var. caulivora and D. phaseolorum var. meridionalis are varieties of D. phaseolorum, and that D. phaseolorum var. sojae may either be several varieties of D. phaseolorum, or possibly several distinct species (Zhang et al., 1998). 3.4 Aggressiveness of Phomopsis longicolla and other Phomopsis spp. isolates on soybean Knowledge about the variability of the pathogen is essential for understanding its population diversity, and such information will also be important for selecting isolates to

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develop broad-based disease resistance in soybean lines. However, there are only a few publications on the variability of aggressiveness on soybean among P. longicolla isolates. The term “pathogen aggressiveness”, as used in this chapter, is based on colonization of and damage to soybean (Agrios, 1999; Shurtleff & Averre, 1997). Mengistu et al. (2009) reported that soybean pods inoculated at the growth stage R7 with two P. longicolla isolates from weeds showed 25% to 30% infection of seeds, while one soybean isolate caused seed infection of 80%. Although the study used only a few isolates, the results indicated that there were real differences in aggressiveness between the isolates. In a recent study, Li et al (2010b) evaluated 48 isolates from the National Soybean Pathogen Collection Center at the University of Illinois at Urbana-Champaign. These included 35 P. longicolla isolates from soybean in eight states in the U.S., along with the type culture of P. longicolla (Fau 600, ATCC 64802) from soybean in Ohio (Hobbs et al., 1985), two P. longicolla isolates from velvetleaf in Illinois (Li et al., 2001), and 11 other Phomopsis spp. isolates from other hosts in four states in the U.S., as well as from Canada and Costa Rica. Prior to the pathogenicity tests, each isolate of P. longicolla was examined for sporulation, dimension of conidia, pattern of stroma, and presence or absence of hyaline, filiform, and hamate beta conidia and perithecia to confirm identification (Hartman et al., 1999). The identifications of soybean isolates were verified previously by sequence analysis of the ITS regions and the mitochondrial small subunit rRNA genes (Li et al., 2001; Zhang et al., 1998). The identities of 11 other Phomopsis spp. isolates from other hosts were confirmed by the USDA-ARS Systematic Botany and Mycology Laboratory in Beltsville, MD (http://nt.ars-grin.gov/ fungaldatabases/specimens/specimens.cfm). Evaluating isolates of seedborne pathogen for aggressiveness based on seed infecting characteristics are practically difficult, especially when working with many isolates. Since P. longicolla can be isolated from all plant parts, the cut-seedling assay measuring stem length and stem lesion length under controlled greenhouse conditions is, however, an easy and effective method to compare isolates and provide quantitative measurements of the infection by isolates on soybean. This method was used to test the pathogenicity of P. longicolla as a new pathogen on velvetleaf not only in the US (Li et al., 2001) but also in Croatia (Vrandecic et al., 2004), plus it was also used to confirm the first discovery of P. longicolla causing soybean stem blight in China (Cui et al., 2009). In the greenhouse pathogenicity tests conducted by Li et al. (2010b), aggressiveness of isolates of P. longicolla from soybean and other Phomopsis spp. from other hosts were compared by inoculating 2week old soybean plants of cv. Williams 82 with them. There were significant (P ≤ 0.0001) differences among isolates based on stem length and stem lesion length. The P. longicolla soybean isolate PL16, from Mississippi, caused the shortest stem length while the nonsoybean isolate P9, from Illinois, caused the greatest stem lesion length. The type isolate of P. longicolla, PL31 (Fau 600), was one of the three most aggressive isolates among the 48 isolates evaluated. The velvetleaf isolate P9 from Illinois was the most aggressive among 13 isolates from non-soybean hosts. This study provided the first evaluation of aggressiveness of P. longicolla isolates from different geographic origins and the first demonstration that Phomopsis spp. isolated from cantaloupe, eggplant, and watermelon was able to infected soybean. This information is very useful for selecting isolates for screening in breeding broad-based resistance in soybean lines to PSD.

4. Disease development and epidemiology PSD occurs in most soybean production areas, especially in the mid-south of the U.S. The incidence and severity of the disease vary year to year, particularly depending on the

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weather conditions (Wrather et al., 2003; 2004). Bradley et al. (2002) observed a greater percentage of harvested seed infection by Phomopsis spp. in most cultivars at Champaign, Illinois in 1999 than 1998. In the southern U.S., because of the drought stress commonly occurring during the bloom and pod fill stages, the Early Soybean Production System (ESPS) was developed to help growers avoid late-season drought damage to soybean (Heatherly, 1999). However, the ESPS creates a concern about seed quarlity because seed infection with Phomopsis spp. is high when early maturity cultivars are grown in the ESPS (Mayhew & Caviness, 1994). A 3-year study was conducted to investigate the effects of planting date and cultivar on seed yield, quarlity, and Phomopsis sp. seed infection (Wrather et al., 2003). It was found that the incidence of PSD was frequently high and the seed quality low in seeds from early-maturing maturity group (MG) III and IV soybean cultivars planted in early- to midApril in the southern U.S. (Wrather et al., 2003). In another study conducted in 1995 to 1997 and in 2001 in the fields at Stoneville, Mississipi, significant effects due to year, irrigation, maturity group (MG), and date of planting (DOP) on the incidence of P. longicolla and seed germination were observed (Mengistu & Heatherly, 2006). Irrigation treatment increased the levels of P. longicolla infection indicating that soil moisture could increase the relative humidity in the canopy in a way that favors PSD development. The incidence of PSD was also associated with total rainfall and rainfall frequency (Mengistu & Heatherly, 2006).

5. Disease management Several control strategies have been used to manage PSD and to reduce the impact of the disease. These include rotating soybeans with non-legume crops, such as corn or wheat, which are not hosts for the fungus, treating seeds with fungicides or applying fungicides during pod-fill, tilling the soil to disrupt spore dissemination, and harvesting mature seeds promptly. Along with these strategies, the use of resistant cultivars is the most effective method for controlling PSD (Jackson et al., 2005; Pathan et al., 2009; Roy et al., 1994), especially when environmental conditions are conducive for disease development. 5.1 Screening for PSD resistance Screening soybean lines for resistance to PSD is the first step toward developing PSDresistant cultivars. A 3-year screening project begun in 2009 has been funded by the United Soybean Board (USB), a group of farmer-directors that administers the organization’s soybean checkoff program, which seeks to strengthen soybean marketing, production technology, and research on new, value-added uses. In this project, hundreds of soybean germplasm accessions representing 28 regions or origins and from MG III to V, breeding lines, and commercial cultivars collected around the world were screened for resistance to PSD in three states (Arkansas, Mississippi, and Missouri) of the U.S. (Li et al., 2010a,c, & d). Seeds, 33 seeds m-1 of row, were planted in 3.04-m long single row plots with a 0.96-m row spacing. The experimental design was a randomized complete block with four replications. Seeds were harvested from each plot when the plants were mature. Seeds from each plot were tested for percent seed infected by Phomopsis spp., percent seed germination, and visual quality using a scale of 1 to 5 (Fig. 3) where 1 = excellent (no bad/infected seed); 2 = good (less than 10% bad/infected seed); 3 = fair ( 11-30% bad/infected seed); 4 = poor (3150% bad/infected seed); and 5 = very poor (more than 50% bad/infected seed). Factors considered in estimating seed quality were: seed wrinkling, molding, mottling, and discoloration. Frequent rainfall during seed maturation led to high levels of seed infection

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by a number of fungi (Fig. 4). Significant differences in seed infection by Phomopsis spp. were observed among soybean lines with some lines having no infection, while others had infection levels as high as 90%. These differences between lines were also reflected in visual

Visual Assessment of Seed Quality

1

2

4

5

1-5 scales

3

Fig. 3. Visual score of seed quality on a 1-5 scale, where 1 = excellent seed (no bad/infected seed) and 5 = very poor seed (more than 50% bad/infected seed).

Fig. 4. Seed infection by a number of fungi were observed in the seed plating assays for the germplasm screening in United States in 2009 (Li et al., 2010 a,c, &d).

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seed quality and seed germination (Fig. 5). Soybean lines with low infection incidences, good visual quality, and high germination rates at all locations were tested along with selected lines having differential responses to PSD cross locations in the 2010 field trials (Li et al., 2010 a,c, & d). In addition, field screenings of over 200 MG V plant introductions were conducted in Stoneville, Mississippi, U.S. from 2006 through 2009 (Li et al., 2009b). Two potential PSD-resistant lines were identified ( Li et al., unpublished).

Fig. 5. Seed germination tests followed the protocol of Copeland, L. O. ed. (1981). The range of seed germination rates was from 10 to 100% for the assays of seed harvested from the germplasm screening in United States in 2009 (Li et al., 2010 a.c, &d).

Fig. 6. Soybean lines without infection (left) and with high levels of Phomopsis spp. infection (right) were identified from the seed plating assay.

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In another screening project (Li et al., 2009a), 50 soybean cultivars were planted at Stoneville, Mississippi to determine their reaction to PSD in 2007. Two lines, SS93-6012 and SS93-6181, previously reported to be PSD-resistant in Missouri were included, and the cultivars Hill and Williams 82 were used as susceptible checks. The seeds of soybean lines selected for planting were generally healthy. Of 50 lines tested, six lines had 100% germination, 30 lines had germination rates ranging from 80% to 97%, and 12 lines had rates ranging from 63% to 77%. Two lines had germination rates of 50% and 53%, respectively. In the seed plating assay, 37 lines had no P. longicolla-infected seed, 10 and three lines had P. longicolla incidences of 3% and 7%, respectively. Incidence of P. longicolla in seeds from inoculated field plots differed significantly (P ≤ 0.05) ranging from 6% to 50% among soybean lines. Several soybean cultivars with lower disease incidence than the PSD resistant lines SS93-6181 and SS93-6013 were identified (Li et al., 2009a). 5.2 Reported sources of PSD resistance Efforts have been made to identify sources of PSD resistance in the past decades. At least twenty-six soybean lines were found to have certain levels of PSD resistance (Table 1). However, some resistant lines identified in other regions were susceptible in Arkansas, Mississippi, or Missouri, U.S. (Li et al., unpublished). It is not known if there is “isolate by location” interaction, but there is a need to identify new sources of resistance to PSD.

6. Inheritance of phomopsis seed decay resistance – case studies To date, inheritance of most reported sources of PSD resistance is still not clear. No gene symbol has been assigned for PSD resistance genes yet. However, there are some reports about the resistance to PSD characterized as qualitative and a number of major dominant genes (Jackson et al., 2005; 2009; Minor at al., 1995; Smith et al., 2008; Zimmerman & Minor, 1993). 6.1 Inheritance of resistance to PSD in soybean PI417479 It was reported that PI 417479, a source of resistance to PSD, was identified by screening approximately 3,000 soybean introductions in MGs III and IV from 1983 through 1985 at the Agronomy Research Center of the University of Missouri, in Columbia, MO, USA and at the Isabela Substation of the University of Puerto Rico at Mayaguez ( Brown et al., 1987). To study the inheritance of PSD resistance in PI417479, crosses were made between PI417419 and each of two PSD-susceptible genotypes (Zimmerman & Minor, 1993). By analyzing PSD incidence on plants from five generations (F1, F2, F3, B1, and B2, in which B1 represented a backcross between the F1 and the resistant parent and B2 represented a backcross between the F1 and the susceptible parents), it was concluded that the PSD resistance in PI417479 was controlled by two complementary dominant nuclear genes. The two resistance genes can thus be transferred using a backcross procedure (Zimmerman & Minor, 1993). 6.2 Inheritance of resistance to PSD in soybean PI80837 and MO/PSD-0259 (PI562694) Ploper et al. (1992) identified PI 80837 having low levels of PSD infection in field trials in addition to its resistance to soybean mosaic virus and purple seed stain (Buss et al., 1979; Roy & Abney, 1988; Wilcox et al., 1975). The PSD-resistant line MO/PSD-0259 derives its resistance from PI417479 (Jackson et al., 2005). To characterize the inheritance of PSD resistance in PI80837 and to determine if it differs from resistance in MO/PSD-0259, PI80837

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Soybean line

Reference

1

PI548438 (Arksoy)

Minor et al., 1995

2

PI80837

Brown et al., 1987

3

PI82264

Walters & Caviness, 1973

4

PI88264

Minor et al., 1995

5

PI181550

Athow, 1987

6

PI200501

Ross, 1986

7

PI200510

Minor et al., 1995

8

PI204331

www.soydiseases.illinois.edu

9

PI205089

www.soydiseases.illinois.edu

10

PI205907

www.soydiseases.illinois.edu

11

PI205908

www.soydiseases.illinois.edu

12

PI205912

www.soydiseases.illinois.edu

13

PI209908

Minor et al., 1995

14

PI219635

www.soydiseases.illinois.edu

15

PI227687

Minor et al., 1995

16

PI229358

Minor et al., 1995

17

PI259539

www.soydiseases.illinois.edu

18

PI279088

www.soydiseases.illinois.edu

19

PI341249

www.soydiseases.illinois.edu

20

PI360835

www.soydiseases.illinois.edu

21

PI360841

Brown et al., 1987

22

PI385942

www.soydiseases.illinois.edu

23

PI417419

Brown et al., 1987

24

PI423903

www.soydiseases.illinois.edu

25

PI562694

Minor et al., 1993

26

PI80837

Ploper et al., 1992

Table 1. A list of reported sources of resistance to Phomopsis seed decay.

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was crossed with each of the two PSD-susceptible lines Agripro 350 (AP350) and PI9113, and with MO/PSD-0259 (Jackson et al., 2005). Populations and lines were screened in the field and Phomopsis infection was assayed by plating seed. Seed infection of reciprocal F1 plants of AP350 × PI80837 was not different from that of PI80837. Data from F2 populations of AP350 × PI80837 and PI91113 × PI80837; and F2:3 lines from AP350 × PI80837 fit models for a single dominant gene in PI80837 that confers PSD resistance. It was found that F2 population data from AP350 × MO/PSD-0259 fit a model for single dominant gene resistance in MO/PSD-0259, and data from an F2 population and F2:3 lines of PI80837 × MO/PSD-0259 fit a model for two different dominant genes. Based on those results, Jackson et al. (2005) concluded that PSD resistance in PI80837 is conferred by a single dominant gene under nuclear control that is different from the gene in MO/PSD-0259. 6.3 Inheritance of resistance to PSD in soybean PI360841 Studies on the inheritance of resistance to PSD in soybean PI360841 were conducted to test populations developed from the crosses between PI360841 and two PSD-susceptible genotypes, Agripro 350 (AP350) and PI91113, to determine the number of genes for PSD resistance (Smith et al., 2008). Other crosses were made between PI360841 and the PSDresistant parents MO/PSD-0259 and PI80837 to test the allelic relationships of the resistance genes (Smith et al., 2008). Seeds from the parents and the F2 population were assayed for Phomopsis infection. Chi-square analysis of F2 data from the resistant × susceptible crosses indicated a good fit to a 9R:7S model for two complementary dominant genes conferring PSD resistance in PI360841. F2 data from MO/PSD-0259 × PI360841 showed a good fit to a 57R:7S model for two complementary dominant genes from PI360841 and a different dominant gene from MO/PSD-0259. Since there was no apparent segregation for resistance in the F2 population derived from PI360841 × PI80837, except for one suspicious susceptible plant, it suggested that one of the genes in PI360841 is allelic to a PSD resistance gene in PI80837 (Smith et al., 2008). This was the first report of a new gene in PI360841 for PSD resistance. This gene, along with other different PSD-resistance genes in MO/PSD-0259 and PI80837 are useful to breeders for developing lines with a high level of resistance to PSD. Further studies have identified simple sequence repeat (SSR) markers linked to genes for PSD resistance in PI80837 and MO/PSD-0259. Jackson et al. (2009) reported that PSD resistance in MO/PSD-0259 and PI80837 is controlled by two different single dominant genes. The gene in PI80837 is located in the vicinity of Sat_177 (4.3 cM) and Sat_342 (15.8 cM) on molecular linkage group (MLG) B2 (chromosome 14). The gene that conditions resistance in MO/PSD-0259 is linked to Sat_317 (5.9 cM) and Sat_120 (12.7 cM) on MLG F (chromosome 13). Identification of these loci and markers linked to them will facilitate marker-assisted selection in breeding programs.

7. Summary Phomopsis seed decay (PSD) of soybean causes poor seed quality and suppresses yield in most soybean-growing countries. The disease is caused primarily by the fungal pathogen Phomopsis longicolla along with other Phomopsis and Diaporthe spp. Infected seeds range from symptomless to shriveled, elongated, cracked, and often appear chalky-white. Seed quality is poor due to reduction in seed viability and oil content, alteration of seed composition, and

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increased frequencies of moldy and/or split beans. Hot and humid environments, especially during the period from pod fill through harvest, favor pathogen growth and disease development. The use of resistant cultivars is the most effective method for controlling PSD. Extensive screening for PSD resistance has resulted in the identification of resistant sources. MO/PSD-0259 carries a single dominant gene for PSD resistance derived from PI417479 whereas resistance in PI80837 is conferred by a different gene. PI360841 carries two complementary dominant genes for PSD resistance; both are different from the gene in MO/PSD-0259 but one of them maps to the same region as the gene in PI80837. Simple sequence repeat (SSR) markers linked to PSD resistance genes in PI80837 and MO/PSD-0259 have been identified. These SSR markers should be useful in selection for resistant genotypes in breeding programs.

8. Acknowledgments The author appreciates the support of the United Soybean Board for the research project (#9261) on Screening Germplasm and Breeding for Resistance to Phomopsis Seed Decay in Soybean as well as support from Drs. Pengyin Chen, John Rupe, and Allen Wrather for their collaboration with the project. Special thanks to Drs. Richard Joost, Stephen Muench, Lawrence Young, Jeff Ray, James Smith, David Walker, and Glen Hartman for valuable discussion about the research on Phomopsis seed decay. The author also thanks Drs. Richard Joost, David Walker, Houston Hobbs, and Xin-Gen Zhou for their thorough reviews of this book chapter. Mention of a trademark or proprietary product does not constitute a guarantee or warranty of the product by the U.S. Department of Agriculture and does not imply approval or the exclusion of other products that may also be suitable.

9. References Agrios, G. N. (1999). Plant Pathology, Academic Press, San Diego, Califrornia, USA. Baird, R. E., Abney, T. S., & Mullinix, B. G. (2001). Fungi associated with pods and seeds during the R6 and R8 stages of four soybean cultivars in southwestern Indiana. Phytoprotection 82:1-11. Balducchi, A. J., & McGee, D. C. (1987). Environmental factors influencing infection of soybean seeds by Phomopsis and Diaporthe species during seed maturation. Plant Dis. 71:209-212. Bradley, C. A., Hartman, G. L., Wax, L. M., & Pedersen, W. L. (2002). Quality of harvested seed associated with soybean cultivars and herbicides under weed-free conditions. Plant Dis. 86: 1036-1042. Brill, L. M., McClary, R. D., and Sinclair, J. B. (1994). Analysis of two ELISA formats and antigen preparations using polyclonal antibodies against Phomopsis longicolla. Phytopathology 84:173-179. Brown, E. A., Minor, H. C. & Calvert, O. H. (1987). A soybean genotype resistant to Phomopsis seed decay. Crop Sci. 27: 895-898. Buss, G. R. Smith, T. J. & Camper, H. M. (1979) Registration of Ware soybean cultivar. Crop Sci. 19: 564.

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Copeland, L. O. (1981) Rules for testing seeds. Association of official seed analysis, Stone Printing Co., Lansing, Michigan, USA. Cui, Y. L. Duan, C. X., Wang, X. M., Li, H.J., and Zhu, Z.D. (2009). First report of Phomopsis longicolla causing soybean stem blight in China. Plant Pathol. 58:799. Farr, D.F., Bills, G. F., Chamuris, G P. & Rossman, A. Y. (1989). Fungi on plants and plant products in the United States. American Phytopathological Society Press, St. Paul, Minnesota, USA. Gleason, M. L. Ghabrial, S. A. And Ferriss, R. S. (1987). Serological detection of Phomopsis longicolla in soybean seeds. Phytopathology 77: 371-375. Harrington, T. C., Steimel, J., Workneh, F., & Yang, X. B. (2000). Molecular identification of fungi with vascular discoloration of soybean in the north central United States. Plant Dis. 84:83-89. Hartman, G. L., Sinclair, J. B., & Rupe, J. C. (1999). Compendium of Soybean Diseases. 4th ed. American Phytopathological Society, St. Paul, Minnesota, USA. Heatherly, L. G. (1999). Early soybean production system (ESPS). In: Soybean Production in the Midsouth. L.G. Heatherly & H.F. Hodges (Ed.) pp. 103-118 CRC Press, Boca Raton, Florida, USA. Hepperly, P. R. & Sinclair, J. B. (1978). Quality losses in Phomopsis – infected soybean seeds. Phytopathology 68:1684-1687. Hobbs, T. W., Schmitthenner, A. F., & Kuter, G. A. (1985). A new Phomopsis species from soybean. Mycologia 77:535-544. Jackson, E.W., Fenn, P, & Chen, P. (2005). Inheritance of resistance to Phomopsis seed decay in soybean PI80837 and MO/PSD-0259 (PI562694). Crop Sci. 45:2400 -2404. Jackson, E. W, Feng, C., Fenn, P, & Chen, P. (2009). Genetic mapping of resistance to Phomopsis seed decay in the soybean breeding line MO/PSD-0259 (PI562694) and plant introduction 80837. J. Hered. 100: 777-783. Kmetz, K.T., C.W. Ellett, & A.F. Schmitthenner (1974). Isolation of seedborne Diaporthe phaseolorum and Phomopsis from immature soybean plants. Plant Dis. Reporter 58:978-982. Kmetz, K.T., C.W. Ellett, & A.F. Schmitthenner (1979). Soybean seed decay: sources of inoculum and nature of infection. Phytopathology 69:798-801. Koenning, S. R. (2010). Southern United States soybean disease loss estimate for 2009. Proceedings of the Southern Soybean Disease Workers, the 37th Annual Meeting, p 1. Kulik, M. M. & Sinclair, J. B. 1999. Phomopsis Seed Decay. In: Compendium of Soybean Diseases. G. L. Hartman, J. B. Sinclair, & J. C. Rupe (Ed.), Pp. 31 – 32 American Phytopathological Society, St. Paul, Minnesota, USA. Lehman, S.G. (1923). Pod and stem blight of the soybean. Ann. Missouri Bot. Gara. 10:111-178. Li, S., Bradley, C. A., Hartman, G. L. & Pedersen, W. L. (2001). First report of Phomopsis longicolla from velvetleaf causing stem lesions on inoculated soybean and velvetleaf plants. Plant Dis. 85:1031. Li, S., Boykin, D., Sciumbato, G., Wrather, A., Shannon, G., & Sleper, D. (2009a) Reaction of soybean cultivars to Phomopsis seed decay in the Mississippi Delta, 2007. Plant Disease Management Rep., DOI: 10.1094/PDMR03: FC096

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(http://www.plantmanagementnetwork.org/pub/trial/PDMR/reports/2009/FC0 96.pdf). 2009. Li, S., Chen. P., Hartman, G. L., Smith, J. & Nelson, R. (2009b). Research highlights on soybean Phomopsis seed decay in the US: pathogen characterization, germplasm screening, and genetic resistance. Abstracts of 2009 World Soybean Research Conference VIII (Beijing, China, August 10-15, 2009), pp. 91-92. Li. S., Chen, P., Rupe, J., & Wrather (2010a). Reaction of maturity group V soybean plant introductions to Phomopsis seed decay in Arkansas, Mississippi, and Missouri, 2009. Plant Disease Management Rep. 4: ST034. Li, S., & Hartman, G. L. (2003). Molecular detection of Fusarium solani f. sp. glycines in soybean roots and soil. Plant Pathol. 52:74-83. Li. S., Hartman, L. G. & Boykin, D. (2010b). Aggressiveness of Phomopsis longicolla and other Phomopsis spp. on soybean. Plant Dis. 94:1035-1040. Li. S., Rupe, J., Chen, P., & Wrather, A. (2010c). Reaction of maturity group IV soybean plant introductions to Phomopsis seed decay in Arkansas, Mississippi, and Missouri, 2009. Plant Disease Management Rep. 4: ST035. Li. S., Wrather, A., Rupe, J., & Chen, P. (2010d). Reaction of maturity group III soybean plant introductions to Phomopsis seed decay in Arkansas, Mississippi, and Missouri, 2009. Plant Disease Management Rep. 4: ST036. Mayhew, W. L. & Caviness, C. E. (1994). Seed quality and yield of early-planted, short – season soybean genotype. Agron. J. 86: 16-19. Mengistu, A., Castlebury, L. A., Smith, J. R., Ray, J. & Bellaloui, N. (2009). Seasonal progress of Phomopsis longicolla infection on soybean plant parts and its relationship to seed quality. Plant Dis. 93:1009-1018. Mengistu, A. & Heatherly, L. G. (2006). Planting date, irrigation, maturity group, year, and environment effects on Phomopsis longicolla, seed germination, and seed health rating of soybean in the early soybean production system of the midsouthern USA. Crop Protection 25: 310-317. Minor, H. C., Brown, E. A., & Zimmerman, M. S. (1995). Developing soybean varieties with genetic resistance to Phomopsis spp. J. Am Oil Chem Soc. 72:1431-1434. Nitimargi, N. M. (1935). Studies in the genera Cytosporina, Phomopsis, and Diaporthe. VII. Chemical factors influencing spore characters. Ann. Bot. (London), 49: 19-40. Parmeter, J. R. (1958). An effect of substrate on spore form in Phomopsis. Phytopathology 48: 396-397. (Abstr.). Pathan, M., S., Clark, K. M, Wrather, J. A., Sciumbato, G. L., Shannon, J. G., Nguyen, H. T., & Sleper, D. A. (2009). Registration of soybean germplasm SS93-6012 and SS93-6181 resistant to Phomopsis seed decay. J. Plant Registrations 3: 91-93. Ploper, L. D., Abney, T. S., & Roy, K. W. (1992). Influence of soybean genotype on rate of seed maturation and its impact on seedborne fungi. Plant Dis. 76: 287-292. Rehner, S.A. & Uecker, F.A. (1994). Nuclear ribosomal internal transcribed space phylogeny and host diversity in the coelomycete Phomopsis. Can. J. Bot. 72:1666-1674. Roy, K. W. & Abney, T. S. (1988). Colonization of pods and infection of seeds by Phomopsis longicolla in susceptible and resistant soybean lines inoculated in the greenhouse. Can. J. Plant Pathol. 10: 317-320.

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Roy, K. W., Keith, B. C., & Andrews, C. H. (1994). Resistance of hard seeded soybean lines to seed infection by Phomopsis, other fungi and soybean mosaic virus. Can. J. Plant Pathol. 16:122-128. Rupe, J. C. (1990). Effects of temperature on the rate of infection of soybean seedlings by Phomopsis longicolla. Can. J. Plant Pathol. 12:43-47. Rupe, J. C., & Ferriss, R. S. (1986). Effects of pod moisture on soybean seed infection by Phomopsis sp. Phytopathology 76:273-277. Shurtleff, M. C., & Averre, C. W. III. (1997). Glossary of Plant-Pathological Terms. American Phytopathological Society, St. Paul, Minnesota, USA. Sinclair, J. B. (1982). Seed testing: Detection of pathogens. In: Soybean Seed Quality and Stand Establishment. INSOY Ser. N0. 22. J. B. Sinclair and J. A. Jacobs (Ed.), pp. 112-115 University of Illinois, Urbana-Champaign, USA. Sinclair, J. B. (1993). Phomopsis seed decay of soybeans - a prototype for studying seed disease. Plant Dis. 77:329-334. Sinclair, J. B. (1999). Diaporthe - Phomopsis. Page 31 in: Compendium of Soybean Diseases. G. L. Hartman, J. B. Sinclair, and J. C. Rupe, eds. American Phytopathological Society, St. Paul, Minnesota, USA. Smith, S., Fenn, P., Chen, P, & Jackson, E. (2008). Inheritance of resistance to Phomopsis seed decay in PI360841 soybean. J. Heredity 99: 588-592. Velicheti, R. K., Lamison, C., Brill, L. M., and Sinclair, J. B. (1993). Immunodetection of Phomopsis species in asymptomatic soybean plants. Plant Dis. 77: 70-73. Vrandecic, K., Cosic, J., Riccion, L., Duvnjak, T., and Jurkovic, D. (2004). Phomopsis longicolla – new pathogen on Abutilon theophrasti in Croatia. Plant Pathol. 53:251. Wilcox, J. R., Laviolette, F. A., & Martin, R. J. (1975). Heritability of purple seed stain resistance in soybean. Crop Sci. 15: 525-526. Wrather, J. A. & Koenning, S. R. (2009). Effects of diseases on soybean yields in the United States 1996 to 2007. Online. Plant Health Progress doi:10.1094/PHP-2009-04401-01RS. Wrather, J. A., Shannon, J. G., Stevens, W. E., Sleper, D. A. & Arelli, A. P. (2004). Soybean cultivar and fungicide effects on Phomopsis sp. seed infection. Plant Dis. 88:721-723. Wrather, J. A., Sleper, D. A., Stevens, W. E., Shannon, J. G. & Wilson, R. F. (2003). Planting date and cultivar effects on soybean yield, seed quality, and Phomopsis sp. seed infection. Plant Dis. 87:529-532. Xue, A. G., Morrison, M. J., Cober, E., Anderson, T. R., Rioux, S., Ablett, G. R., Rajcan, I., Hall, R., & Zhang, J. X. (2007). Frequency of isolation of species of Diaporthe and Phomopsis from soybean plants in Ontario and benefits of seed treatments. Can. J. Plant Pathol. 29:354-364. Zhang, A. W., Hartman, G. L., Riccioni, L., Chen, W. D., Ma, R. Z., & Pedersen, W. L. (1997). Using PCR to distinguish Diaporthe phaseolrum and Phomopsis longicolla from other soybean fungal pathogens to detect them in soybean tissue. Plant Dis. 81:1143-1149. Zhang, A. W., Hartman, G. L., Riccioni, L., Pedersen, W. L. & Hartman, G. L. (1998). Molecular identification and phytogenetic grouping of Diaporthe phaseolrum and Phomopsis longicolla isolates from soybean. Phytopathology 88:1306-1314.

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14 Soybean Rust: Five Years of Research Arianne Tremblay USDA-ARS-Plant Science Institute U.S.A. 1. Introduction Soybean rust (SR), Phakopsora pachyrhizi Sydow is an obligate biotrophic fungal pathogen causing rust on a wide range of hosts including many kind of beans (Glycine max, Phaseolus vulgaris var vulgaris, P. lunatus var lunatus, P. coccineus, Vicia faba, Lablab purpureus, Vigna radiata, V. unguiculata, Psophocarpus tetragonogobus, Pachyrhizus ahipa, P. erosus), few kind of peas (Pisum sativum, Cajanus cajan), many kinds of clover (Alisycarpus vaginalis, Trifolium repens, T. incarnatum, Melilotus officinalis), fenugreek, lupines, trefoil and kudzu. It originated in Asia and emerged in the continental United States (Louisiana) in 2004 (Scheinder et al., 2005) following hurricane Ivan which brought SR spores from Columbia. Presence of SR in South America since 2001 has been responsible for large yield losses in soybean, Glycine max L. Merr., fields. For example, in Brazil, a recent study depicted a two-year field trial where soybean rust was responsible for 37% to 67% of soybean seed yield losses (Kumudini et al., 2008). This study agreed with yield losses already observed in Asia, where the disease originated. Losses there can reach up to 80% (Miles et al., 2003). Based on a disease risk assessment study conducted by Pivonia and Yang (2005), climatic conditions in the soybean producing regions of the United States are suitable for similar yield losses. In 2009, soybean rust has been detected in sixteen states including 576 counties along the southeastern portion of the country (USDA Integrated Pest Management (IPM) web site; http://sbr.ipmpipe.org/cgi-bin/sbr/public.cgi). Its location boundaries in the US are mostly due to its warm location preference in addition to the presence of an alternative host, kudzu, in which it can overwintered. The invasive kudzu plant is an alternative hosts and its massive presence in several locations in South Louisiana and along the Mississippi river makes these locations of choice for soybean rust. Numerous studies have been conducted to build a system model allowing prediction, detection and evaluation of the presence of SR in soybean fields (Tao et al., 2009; Roberts et al., 2009). 1.1 Phakopsora pachyrhizi life cycle The lifecycle of P. pachyrhizi is typical of the majority of other rust fungi with some particularities. Spores, named uredospores, are transported readily by air currents and can be disseminated hundreds of miles in a few days. Weather conditions determine when the spores begin their infection cycle, since they need a period where the temperature fluctuates between 15°C and 28°C with high humidity for germination (Melching et al., 1989). When germination occurs, the uredospore produces a single germ tube that grows across the leaf

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surface until an appressorium forms. Appressoria form over anticlinal walls or over the center of epidermal cells, but rarely over stomata, in contrast to the habit of many other rusts. Thus, penetration is direct rather than through natural openings or through wounds in the leaf tissue. Penetration hyphae, stemming from the appressorium cone, pass through the cuticle to emerge in the intercellular space where a septum is formed to produce the primary hyphae. The primary hypha grows between spongy mesophyll cells where it forms the haustorium. This new infection structure is a specialized organ produced to allow fungi to obtain nutrients from the host. Other functions such as suppression of host defense responses, redirection or reprogramming of the host’s metabolic flow, and biosynthesis might be associated with this specialized organ, although direct evidence to date is lacking (Voegele et al., 2004). Once this first stage has been reached, additional hyphae emerge and spread through the apoplast where many other haustoria are formed. At approximately 5 days after infection, some necrosis of epidermal and mesophyll cells occurs that is visible at the upper surface of the leaves as yellow mosaic discolorations. These symptoms appeared the same on a resistant and a susceptible cultivar (Figure 1A). Hyphae aggregate and a uredinium arises in

Fig. 1. SR symptoms observed on soybean leaves. (a) Yellow mosaic discoloration observed at 7 dai. (b) Tan lesions observed at 21 dai and (c) reddish-brown lesions observed at 14 dai. Photos were taken at the United States Department of Agriculture-Agricultural Research Service Stoneville Research Quarantine Facility in Mississippi.

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the spongy mesophyll cell layer. Uredinia can develop 5 to 8 days after infection by a uredospore and its development might extend up to 4 weeks. The first uredospores produced by the uredinia arise 9 to 10 days after initial infection. Figure 2 shows SR development on a leaf. Spore production can be observed for up to 3 weeks. Secondary uredinia may arise on the margins of the initial infection and extend spore production for an additional 8 weeks. Thus, from an initial infection, there can be first generation uredinia that maintain sporulation for up to 15 weeks (Koch et al., 1983; Mendgen and Hahn, 2002). This really high rate of sporulation is typical of a susceptible reaction where lesions on the upper surface of the leaf are tan (Figure 1B). Plants classified as resistant developed a dark, reddish-brown lesion with few or no spores (Figure 1C) (Hartwig and Bromfield, 1983). Figure 2 from Hahn (2000) depicted the entire soybean rust life cycle.

Fig. 2. Internal structure of a typical dicotyledon leaf showing the different cell layers and infection by a rust fungus. GT, germ tube; AP, appressorium; PH, penetration hyphae; IH, infection hyphae; H, haustorium. Schema was taken from Hahn (2000). 1.2 Preventing soybean rust 1.2.1 Fungicides application Cultural practices such as planting date, row width, and crop rotation sequences have little or no effect on soybean rust, so fungicides are the only option for managing soybean rust until disease-resistant varieties are developed (Shaner et al. 2005). However, there are currently only four chemicals fully registered by the U.S. Environmental Protection Agency for foliar application on soybean. Fungicide application also is expensive and does not always control the pathogen from flowering through pod fill. Timing of foliar fungicide applications often is the key factor that determines success or failure to control fungal plant pathogens. For soybean, fungicide timing has been shown to be critical for the control of some foliar diseases. For example, the control of frogeye leaf spot of soybean (Cercospora sojina) varied with applications of benomyl at different reproductive growth stages of the crop (Akem, 1995). To manage soybean rust with fungicides, three strategies include applying fungicides in a predetermined calendar-based schedule (Levy, 2005; Miles et al., 2007), scouting and applying fungicide after first detection of soybean rust, and utilizing a forecast system that monitors disease development in areas that are potential inoculum sources and applying fungicides ahead of a predicted deposition of spores. A calendarbased program with two or three applications provides the greatest level of yield protection; the crop is protected from flowering through grain fill; however, this may result in

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unnecessary fungicide applications that increase production costs and may have unforeseen consequences due to activity on non target fungi. Since the appearance of SR in the U.S. continental, studies were mostly conducted in the aim of finding the best fungicide, the right application time and the best way to do it (Mueller et al., 2009; Zhu, 2008; Miles et al., 2007). 1.2.2 Resistance gene discovery Until now, studies have also been focused on genetic approaches to localize quantitative trait loci (QTLs) on different genotypes of soybean and associate those to resistance soybean genes against soybean rust. Scientists have screened over sixteen hundred soybean accessions for resistance or tolerance to soybean rust (SR) (Miles et al., 2006 and 2008). Five resistance loci have been found (Rpp1-Rpp5) in five different accessions. Four have been known since at least the mid-1980s; Rpp1 (McLean & Byth, 1980), Rpp2 and Rpp3 (Hartwig & Bromfield, 1983) and Rpp4 (Hartwig, 1986). More recently, a new dominant resistance allele, Rpp5, was reported by Garcia et al. (2008) and a recessive allele as well (Calvo et al., 2008). Other recent research identified a new allele (Rpp1b, Chakraborty et al., 2009) of a known gene. One more rust resistance gene, Rpp?, was identified and mapped by Monteros et al. (2007). However, none are present in soybean cultivars commercially grown in the United States. Moreover, there are many isolates of P. pachyrhizi and genes conferring resistance to all isolates have not been found which mean that efforts need to be concentrated on finding alternative control measures. From this perspective, understanding the molecular mechanism of the infection process of rust fungi is critical for developing resistant soybean using biotechnology. 1.3 Understand soybean-soybean rust interaction Only few molecular and biological analyses have been done on P. pachyrhizi and on the interaction between P. pachyrhizi and its soybean host. One study analyzed gene expression within P. pachyrhizi germinating spores (Posada-Buitrago and Frederick, 2005). Four hundred eighty-eight unique expressed sequence tags (ESTs) were generated. One hundred eighty-nine of these ESTs showed significant similarities (e-value ≤10-5) to sequences deposited in the NCBI non redundant protein database. This represents only 39% of the entire library. This highlights the scarcity of genomic information available from pathogenic fungi at that time. These genes were assigned putative roles in many different functional categories. Gene encoding a protein similar to the dehydroshikimate dehydrogenase involved in shikimate pathway leading to the biosynthesis of aromatic amino acids may be use to metabolize metabolic intermediates as alternative carbon sources. Genes encoding homologs of translation initiation factors and elongation factors involved in gene and protein expression suggest that protein synthesis is required for spore germination. Genes encoding a chitin deacetylase, an acetylxylan esterase and a chitin synthase may be involved in cell structure and growth as important for dissolution and formation of the cell wall. Genes encoding some calmodulin kinase homologs involved in cell signaling and cell communication may suggest a calcium-signaling pathway which regulates urediniospore germination as seen in other filamentous fungi. Approximately 30% matched hypothetical proteins or proteins with unknown function. Since then, 47,732 additional ESTs from four different cDNA libraries (uredospores collected from infected soybean leaves, more germinating uredospores on water surface, whole soybean leaf tissue 6-8 days after infection (dai) and, whole soybean leaf tissue 13-15 dai) have been deposited in the NCBI database.

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More recently, host gene expression has been studied in whole soybean leaves infected with P. pachyrhizi using whole genome Affymetrix microarrays of soybean. Panthee et al. (2007) found 112 genes significantly differentially expressed, over 35,611 unigenes present on the array, in soybean plants inoculated with P. pachyrhizi 72 h after infection (hai). Forty-six of these genes were up-regulated and 66 were down-regulated. From the up-regulated genes, 14 did not share similarity to known proteins. Most of the up-regulated genes showing similarity to known genes were associated with general defense and stress responses such as salicylic acid (SA)-related protein, heat shock proteins, a leaf senescence-associated receptorlike protein kinase, a glutathione S-transferase, and a chalcone isomerase. Since, they used a soybean cultivar with no known host response to soybean rust, this up-regulation of genes indicated that plants attempt to mount a defense to the pathogen but this was ineffective. There were 20 down-regulated genes that did not share similarity to known proteins. Most of the down-regulated genes encoded peroxidase-related proteins. These proteins catalyzing oxidation-reduction reactions are constitutively expressed in plants and they may be downregulated by the host during an infection process to divert resources to more pressing needs. van de Mortel et al. (2007) added more information by studying soybean response to soybean rust on a susceptible and a resistant host plant at 6, 12, 18, 24, 36, 48, 72, 96, 120 and 168 hai. They found 470 probe sets significantly differentially expressed in both genotypes, whereas 1,046 and 424 probe sets were unique to either the susceptible or the resistant genotypes, respectively. They also described biphasic mRNA changes in soybean in response to SR infection. Within 12 hai, differential gene expression peaked in both genotypes and most (62%) of the common probe sets showed similar expression profiles corresponding basically to nonspecific recognition of SR and activation of basal defense. For example, genes involved in the flavonoid biosynthesis pathway associated to plant defense response were found. Many transcription factors also have been found differentially expressed in a biphasic manner, specifically WRKY transcription factors which may be up or down-regulated. Since WRKY transcription factors change host gene transcription to modulate defenses, this differentially expression suggested complex positive and negative regulation of soybean defense pathways. By 24 hai, mRNA expression of these genes mostly returned to levels found in mock-inoculated plants. This lack of differential expression may come from the low fungal growth at this stage of infection and from the inhibition of the early host responses in the plants. However, a second phase of strong differential gene expression was observed in both genotypes at late stages of infection, which started earlier in the resistant genotype (72 hai) than in the susceptible genotype (96 hai). At these times, only 6% of the common probe sets showed similar expression profiles. Genes encoding components of a sugar transporter superfamily and monosaccharide transporters including a sorbitol transporter as well as genes encoding sugar metabolism enzymes including citrate synthase, isocitrate dehydrogenase, fructose-1,6-bisphosphatase, UDP-arabinose 4epimerase, and trehalose-6-phosphate synthase have been mostly found differentially expressed in the susceptible genotype and may help to provide nutrients to support fungal infection. Soria-Guerra et al. (2009) studied gene expression in Glycine tomentella along a time-course of infection with SR (12, 24, 48 and 72 hai) during a compatible and an incompatible interaction using a 70-mer long-oligo soybean microarray representing about 38,400 genes covering wide developmental stages and physiological conditions. This wild perennial soybean species, as well as others (G. tabacina and G. argyrea) has been reported as resistant to SR. A total of 1,342 genes were found to be differentially expressed at four time-points in resistant

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and susceptible genotypes. About 70% were up-regulated while about 30% were downregulated at all time-points. As previous studies, most of the up-regulated genes belonged to those with metabolic and defense-related functions in both genotypes, and included enzymes encoding phenylalanine ammonia-lyase involved in phenylpropanoid metabolism, chalcone synthase involved in flavonoid biosynthesis, 4-coumarate-CoA ligase involved in phenylpropanoid metabolism as well as jasmonic acid biosynthesis and lipoxygenases involved in jasmonic acid biosynthesis which increased in expression at 12hai in the resistant and at 24hai in the susceptible reaction. They also found pathogenesis-related genes associated with the development of systemic acquired resistance and encoding antimicrobial proteins up-regulated in the resistant genotypes and down-regulated in the susceptible up to 48hai. Genes involved in cellular communication increased in transcript abundance at all first three time-points in both genotypes has been observed. This included genes encoding protein kinases and zinc fingers all related to the establishment of innate immune response. Also consistent with previous studies, many WRKY transcription factors have been found basically up-regulated later in the susceptible genotype then in the resistant one. Most of the up-regulated genes belonged to the resistant genotype while the down-regulated genes belonged to the susceptible genotype. Overall, all these studies were focusing on gene involved in direct defense response while many more genes encoding proteins involved in amino acid metabolism, nucleotide metabolism or protein destination may be targeted to disturb fungal growth. Based on what has already been observed about differences between gene expression in infected areas and non infected area on a single infected leaf in regard to photosynthesis (Scholes and Farrar, 1985; Buchanan et al., 1981), we can argue that avoiding background coming from none infected area may provide a better idea about what is really happening at the infection site of the plant in response to the pathogen. Laser capture microdissection (LCM) is a useful tool for isolating infected cells from non-infected cells. Also, the limited number of studies on pathogen infection structures comes from the difficulty in isolating infection structures from the plant. As an obligate biotroph, the fungus cannot be grown on culture medium; it needs live material in which it can proliferate and it can be difficult to separate fungal cells from host cells. LCM can be used to isolate specific fungal infection structures and minimize plant background. LCM has been used relatively recently in plant research to study gene expression in different plant tissues (Kerk et al., 2003) and also to study the interaction between root-knot nematodes, Meloidogyne spp., and their host Lycopersicon esculentum (Ramsay et al., 2004). An extensive study has been done on the interaction of the soybean cyst nematode, Heterodera glycines, with soybean plants using LCM, wherein nematode feeding sites (syncytia) were isolated from soybean roots. Expression levels in those roots were determined using EST and microarray analysis (Klink et al., 2007, 2010). van Driel et al. (2007) succeeded in isolating septa from hyphae of the filamentous basidiomcyete Rhizoctonia solani grown on an artificial medium. Also, Tang et al. (2006) succeeded in isolating individual maize stalk cells associated with Colletotrichum graminicola hyphae at an early stage of infection and they identified a number of fungal transcripts associated with early infection of stalk parenchyma by microarray analysis. Kankanala (2007) used positioning ablation laser microdissection (PALM) technology and succeeded to isolate and purify biotrophic invasive hyphae of Magnaporthe oryzae 34 hai of rice plants. Both up and down-regulated genes were identified in invasive hypha relative to mycelium, grown in 3 g yeast extract, 3 g casamino acids, 3 g sucrose liquid medium, using M. oryzae microarrays.

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The main objectives of our study was to improve understanding of SR infection on soybean plants by analyzing (i) the gene expression profile in one of the latest fungal infection structures, the uredinium, and (ii) the soybean genes expression pattern at late stage of infection (240 hai). To achieve these objectives, working on a susceptible genotype, we first constructed a cDNA library from selected uredinium cells of soybean leaves infected with P. pachyrhizi isolated using LCM. Resulting sequences allowed identification of specific fungal genes expressed during the infection process. Second, we isolated soybean mesophyll and palisade layer cells using LCM based on morphological changes in the tissues. RNA was extracted from these cells; then cDNA was synthesized, fluorescently labeled and hybridized to a soybean GeneChip® containing 37,744 Glycine max probe sets. Microarray results identified sets of genes whose expression patterns show significant alterations in SR inoculated plants. Third, the utilization of a deep sequencing Solexa/Illumina platform on the mesophyll/palisade layer cell samples was tested to analyze the potential offered by this new technology to identify more fungal genes as well as more soybean genes involved in the infection process. Genes identified as involved in the infection process may be used to broaden soybean resistance to P. pachyrhizi.

2. Identification of SR genes involved in soybean infection 2.1 Methodology A Phakopsora pachyrhizi isolate (MS06-1) was obtained from urediniospores harvested from field-collected kudzu leaves in Jefferson County, Mississippi in August 2006 and its identity was confirmed by microscopy, enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR) as previously described (Li et al., 2007). Urediniospores were increased on a susceptible soybean cultivar, Williams 82 (Hyten et al., 2009) in the Stoneville Research Quarantine Facility in Mississippi. The isolate was then purified by picking up a single uredinium using a fine needle under an Olympus SZX12 dissecting microscope, and reinoculating it on leaves of Williams 82. Urediniospores from this purified culture were harvested using a Cyclone Surface Sampler (Burkard Manufacturing Co. Ltd) connected to a vacuum pump, beginning 240 to 336 hai and continuing at weekly intervals. Inoculum was prepared using freshly collected urediniospores from Williams 82. Spore suspensions were filtered through a 100-μm cell strainer to remove any debris and clumps of urediniospores. Urediniospores were diluted to a final concentration of 1.1 x 105 spores/mL. Three plants per 10 cm-pot with three replicates (pots) were inoculated using 3week-old seedlings of Williams 82. Inoculant was applied with a Preval sprayer (Younkers) at rate of one milliliter of spore suspension per plant. The same solution minus spores was used for a mock inoculation on three pots of plants to monitor the infection. After inoculation, plants were placed in a dew chamber in the dark at 22ºC overnight (approximately 16 h) and then moved to Conviron growth chambers where temperatures were maintained at 23ºC during the day and 20ºC at night under a 16-h photoperiod with a light intensity of 280 μEm-2 s-1. SR inoculated and mock-inoculated plants were kept in two different growth chambers. Soybean leaf tissue was harvested 240 hai. The leaf tissue was cut into 1 cm square pieces and vacuum-infiltrated with Farmer’s solution (FS) composed of 75% ethanol and 25% acetic acid (Sass, 1958) at room temperature for 1 h. Fresh FS was added to the samples and tissue was subjected to an incubation step of 12 h at 4°C. The fixative was removed, and leaves were dehydrated with a graded ethanol series (75%, 85%, 100%, 100%), 30 min each. Ethanol

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was replaced with 1:1 xylene:ethanol for 30 min, followed by three, 100% xylene incubations (30 min each). Xylene was then replaced with paraffin by placing the specimens into a 58°C oven and infiltrating the leaves sequentially in 25, 50 and 75% (1x) and 100% (3x) Paraplast + tissue embedding medium (Tyco Healthcare Group LP) in each step for 3 h. Tissue was cast and mounted for sectioning. Serial sections of leaves were made on an American Optical 820 microtome (American Optical Co.) at a section thickness of 10 μm. Serial sections were placed onto a pool of RNAse-free dH2O, on Leica PEN-Membrane 2.0 uM membrane slides. About five million square microns surface of uredinium sites from infected leaves (Figure 3A-B) were collected by LCM 240 hai using a Leica ASLMD microscope (Leica®). RNA was extracted and isolated from LCM-collected uredinia cells using an isolation Kit specific for small RNA quantity (Arcturus Bioscience Inc.). A unidirectional cDNA library was constructed using an improved method for library construction from microdissected cells (Peterson et al., 1998). Sequencing was conducted using BigDye 3.1 chemistry (Applied Biosystems). Reactions were analyzed on an Applied Biosystems 3730XL sequencer. Sequence base-calling was performed using PHRED (http://www.phrap.org) (Ewing and Green, 1998; Ewing et al., 1998) with a quality score of 20 (99% confidence). Vector and low quality sequences were removed using Lucy (Chou and Holmes, 2001). The trimmed sequences were assembled into consensus sequences using CAP3 (Huang and Madan, 1999) to reduce redundancies.

Fig. 3. LCM of uredinium site from soybean leaf 10dai with soybean rust. (A) Longitudinal section of soybean leaf with a uredinium on the lower leaf surface before LCM visualized at 40X magnification. (B) The same section after LCM with the microdissected uredinium region absent from the section. (C) Longitudinal section of soybean leaf showing brown coloration inside the mesophyll/palisade cells before LCM visualized at 40X. (D) The same section after LCM showing that the microdissected palisade region is absent from the section. Scale bar represents 50 µm.

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The data presented here represents the later assembly process and the resulting contigs and singletons were designated as unisequences. Appropriate Paracel Blast (Striking Development, http://www.paracel.com/) algorithms were used to analyze the contigs and singelton sequences against the following databases: NCBI Vector (current to 2006), NCBI nr nucleotide (current to December, 2007), NCBI nr protein (current to December, 2007), NCBI est-others (current to December, 2007), NCBI Glycine max trace (ftp://ftp.ncbi.nih.gov/pub/TraceDB/glycine_max current to December 2007), NCBI Phakopsora pachyrhizi trace (ftp://ftp.ncbi.nih.gov/pub/TraceDB/phakopsora_pachyrhizi) current to 16 June 2004). 2.2 Results and discussion Of the five cDNA libraries from P. pachyrhizi represented in NCBI public databases, all came from either uredospores germinating on water or from non-germinating or whole infected leaf tissue. This work represents the first LCM-derived cDNA library specifically enriched from the uredinium stage of SR development. This reddish or black pustule-like structure comprises the uredospores-producing fruiting body that is formed on the tissue of infected plants. At 240 hai the uredinium contains uredospores at all developmental stages, not only the mature stage. It also contains other kinds of fungal cells including peripheral paraphyses (Berndt, 2005; Hernandez et al., 2003). Unlike germinating spores, which are rapidly growing and preparing to enter the host, uredinia produce spores for dispersal and survival of a new generation. Hence, genes involved in spore production, spore development, and genes expressed in mature uredospores can be found. A total of 925 clones were sequenced from both ends of the cDNA inserts. The generated sequences resulted in an average of 568 nucleotides readable after editing. The term unisequences is used to identify the assembled products regardless if they are represented by contigs or singletons. A total of 130 unisequences from CAP3 were identified of which 70 were singletons and 60 were contig assemblies representing multiple clones at frequencies ranging from 2 to 431. Over the 130 unisequences identified, 117 displayed similarity to sequences available in NCBI Phakopsora pachyrhizi trace, EST-others and nucleotide databases (nucleotide blast; cut-off set e-value ≤ 10-5). Thirteen unisequences displayed similarity to sequences from Glycine max trace, EST-others and nucleotide databases (nucleotide blast; cut-off set e-value ≤ 10-5). Forty unisequences with significant hits to the NCBI nr protein database (blastx; cut-off set e-value ≤ 10-5) were grouped according to their putative function (Table 1). Approximately half (21) of those unisequences correspond to hypothetical proteins of unknown function (Table 1). For deduced protein sequences that lacked significant similarity to proteins or sequence translations from GenBank, an extra comparison was done against the InterPro database of protein domains and functional sites (http://www.ebi.ac.uk/Tools/InterProScan/), revealing the presence of conserved domains in several unisequences (Table 1). The expression of seventeen unisequences out of the forty with significant homology, chosen for their high homology with proteins or conserved protein domains that might be involved in the infection process, or their high homology with other ESTs found in a uredospore library of P. pachyrhizi isolate TW 72-1, have been analyzed using quantitative PCR at eight additional time-points (time-zero, 2, 7, 20, 24, 48, 96 and 168 hai) (data not showed).

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Unisequence Singleton61

Contig6

Singleton30

Contig58

Contig 24

Singleton48

Contig 38

Contig54 Singleton1

Contig10

Singleton44

Singleton22

Contig17 Contig27

Accession No.

Description

Species

e-value Clone (10-X) frequency

Metabolism TonB-dependent Escherichia coli EAY48797 -92 vitamin B12 receptor Energy Isocitrate Cryptococcus XP_567378 dehydrogenase neoformans var. -57 neoformans (NAD+) Cell growth, cell division and DNA synthesis activator 1 37 kDa Neurospora crassa -70 EDO65226 subunit Transcription Histone Pichia stipitis ABN66688 acetyltransferase -17 SAGA Protein synthesis Arabidopsis Ribosomal protein AAA84030 -25 thaliana S17 Protein destination dolichyl-phosphateDictyostelium EAL73572 mannose alpha-1,3-10 discoideum mannosyltransferase Putative Pisum sativum senescenceBAB33421 -35 associated protein Cellular biogenesis C. neoformans Nucleosome AAW43806 -35 var. neoformans assembly protein Penicillium ABH11422 Peroxin16 -23 chrysogenum Cellular communication/signal transduction Serine/threonineXP_00154100 Ajellomyces protein phosphatase -56 capsulatus 1 PP1 Cell rescue, defense, cell death and ageing Neosartorya UV excision repair EAW24135 -12 fischeri protein (RadW) Cellular organization Conocephalum -49 BAD90781 Histone 3 conicum Transposon, insertion sequence and plasmid proteins Anopheles -14 AAD03794 Tc1-like transposase gambiae Retrotransposon Oryza sativa -05 ABG66123 protein

1

2

1

2

12

1

3

97 1

11

1

1

8 3

303

Soybean Rust: Five Years of Research

Unisequence

Accession No.

Description

Species

Transposase

Wolbachia endosymbiont

e-value Clone (10-X) frequency

Contig33

3 EAL59155

Singleton60 Contig17 Singleton49 Singleton52

1 AAD03794

ABX34586 AAZ28943

Contig1

EDO03956

Contig2

EAL20633

Contig3

CAN72993

Contig7a

EAU89149

Contig9a

EAU89356

Contig28a

EAU80277

Contig35

EAU84377

Contig39a

EDN29539

Contig42a

EDJ99713

Contig43

CAK45797

Tc1-like transposase

Anopheles gambiae

Unclassified putative phage major head protein

Delftia acidovorans Phanerochaete polyprotein chrysosporium Sclerotinia Hypothetical sclerotiorum protein SS1G_06437 C. neoformans Hypothetical protein CNBE2980 var. neoformans Hypothetical Vitis vinifera protein Coprinopsis Chromo-domaincinerea okayama like C. cinerea Transposase okayama Tc1/Tc3 C. cinerea DNA/RNA okayama polymerase C. cinerea Hypothetical okayama protein CC1G_01373 Botryotinia LDOC1 related fuckeliana Retrotransposon gag Magnaporthe grisea protein Hypothetical Aspergillus niger protein An08g11260

Singleton56a Contig46 Singleton10 Singleton12

-14

8

-57

1

-11

1

-06

11

-05

27

-06

6

-38

6

-21

6

-43

3

-24

2

-14

2

-15

3

-07

31

-09

Contig44a

Singleton36a

-36

EAU87561

EDR00494 XP_00150162 0 CAN73669

Signal peptide

Predicted protein Hypothetical protein Hypothetical protein

C. cinerea okayama

-06

3

1 1

Laccaria bicolor

-20 -09

Equus caballus

-07

1

V. vinifera

-13

1

6

304 Unisequence Singleton13 Singleton14a Singleton28 Singleton55 Singleton67a Singleton68

Soybean - Molecular Aspects of Breeding

Accession No. EDP41532 EAU91239 EAK86148 ABQ37728

Description

Species

Malassezia hypothetical protein globosa MGL_4081 C. cinerea Phospholipaseokayama related hypothetical protein Ustilago maydis UM04768.1 hypothetical protein Bradyrhizobium sp. BBta_5781

e-value Clone (10-X) frequency -08

1

-19

1

-20

1

-08

1

EDN30101

Signal peptide

B. fuckeliana

-11

1

EDN03050

predicted protein

A. capsulatus

-12

1

Table 1. Unisequences displaying similarity (e-value ≤ 10-5) to proteins in the nonredundant protein NCBI database, grouped in functional categories. a Unisequence displaying similarity to an hypothetical protein but displayed similarity to a specific conserved domain. Libraries constructed from whole infected plants typically contain many more plant genes than fungal genes. By using LCM to collect infection sites, we reduced the number of plant ESTs versus fungal ESTs generated from infected leaf tissue. Sixty-three percent of the unisequences shared similarity to proteins from fungi (including yeast) while 16% encoded plant protein sequences. The other 21% encoded protein sequences with higher similarity to other organisms, including bacteria, viruses, insects and other invertebrates. These percentages are similar to results obtained by other research groups studying filamentous fungi (Lee et al., 2002a; Trail et al., 2003). Twenty-nine of the 40 unisequences presented in Table 1 are novel P. pachyhrizi sequences. While eleven unisequences corresponded to existing P. pachyhrizi sequences, none matched the sequences described by Posada-Buitrago and Frederick (2005) from a cDNA library from mature spores. Considering that this library is one of the few P. pachyrhizi cDNA libraries that have been analyzed thus far, our results demonstrate that a different gene set is expressed in uredinium compared to germinating spores. The infection process includes three major steps: germination and penetration, proliferation and sporulation (Solomon et al., 2003; Zhang et al., 2008). At 240 hai, sporulation occurs. Identifying genes involved in sporulation events is a necessary first step toward using disruption of these genes as a way to prevent disease dissemination. Several of the unisequences from the uredinium-enriched library were identified as having similarity to proteins or domains that are consistent with functions known to be necessary for sporulation. Fungi need energy at different stages of development. For aerobic fungi, the oxidation of glucose or fatty acids into the tricarboxylic acid cycle can provide up to 38 ATP molecules. One enzyme involved in this cycle is isocitrate dehydrogenase (Contig6), which catalyzes the oxidative decarboxylation of isocitrate to produce α-ketoglutarate and carbon dioxide while it converts the NAD+ coenzyme into NADH. NADH can interact with the electron transport chain, resulting in the production of ATP (Isaac, 1992). Contig6, which showed significant sequence similarity to isocitrate dehydrogenases, was expressed most highly at 20, 48 and 240 hai. In rust fungi, the initial haustorial mother cell typically forms between 24

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and 48hai, consistent with the time frame of Contig6 expression. Jakupović et al. (2006) found many different genes involve in energy production expressed in a haustorium cDNA library. But at 240 hai, the uredinium needs energy to produce uredospores. Reversible protein phosphorylation is a key mechanism to modulate the activity of proteins. It plays a central role in a variety of types of cellular regulation, including control of metabolism, cell cycle, cell proliferation, and differentiation (Stark, 1996). Protein phosphorylation can be conducted by enzymes such as protein kinase C, cyclic AMP (cAMP)-dependent protein kinase, mitogen-activated protein (MAP) kinase, and type 1, 2A, and 2B serine/threonine phosphatases. Clones representing transcripts of some of these enzymes have been found in multiple cDNA libraries from filamentous fungi (Sacadura and Saville, 2003; Zhang et al., 2008). Our results indicate that Contig10 has similarity with a serine/threonine protein phosphatase PP1 from Ajellomyces capsulatus, while Contig41 encodes a serine/threonine protein kinase domain. Formation and differentiation of uredospores within the uredinium requires several proteins that are found during normal growth (Hall et al., 1999; Lengeler et al., 2000). Since protein phosphorylation is involved in many differentiation processes, PP1 might be one of the important enzymes playing a role in spore differentiation at 240 hai. Expression of Contig10 at 2hai might be involved in a quick cell proliferation at the beginning of the infection. The DNA sequence of Contig13 is 100% identical to an EST found in P. pachyrhizi TW 72-1 germinating spores, but the function of that EST is unknown. However, this unisequence is expressed in the early steps of the infection process (between 2 and 24 hai). At 240 hai, this unisequence is expressed at higher levels than at the beginning of the infection and higher than the other unisequences tested by quantitative PCR. It would be expected that all of these genes would be strongly expressed at 240 hai, because they were derived from the uredinia collected at that time. Some of the genes we identified not only are expressed at 240 hai but at other time points as well, indicating that they have important roles during infection and growth, as well as during spore development. van de Mortel et al. (2007) found a distinct biphasic change in mRNA expression in soybean in response to SR. If this kind of response can be observed in soybean, it is possible that a similar response also occurs in the pathogen. One possible explanation is that at the beginning of infection, the pathogen concentrates its energy and effort on infecting the leaf, and many different genes are needed at this stage. Thus, this step may represent the first burst of gene expression. The host plant responds to this infection by its own first burst of gene expression. Then the pathogen proliferates within the plant leaf, coinciding with a second burst of gene expression. Therefore, Contig13 might represent an important gene involved in many steps during fungal infection, proliferation and reproduction. Contig39, which contains a domain similar to LDOC1 domain, is expressed in the plant before the inoculation. LDOC1 domain in mammalian cells was found to be involved in negative regulation of cell proliferation and in differentiation of cancer cells (Nagasaki et al., 1999). The presence of this kind of domain in plants or fungi has not been described previously, but its expression in our library along with its known function in mammalian cells attracts attention. Protein secretion is a critical process in the life cycle of fungi, and filamentous species display a natural ability to secrete large amounts of native proteins. For example, many Avr proteins are secreted from extracellular fungal pathogens, as occurs with bacterial Avr proteins (Luderer et al., 2002; Rep et al., 2004). In biotrophic fungi such as rust and mildew, virulence and avirulence factors and pathogenicity mechanisms are difficult to study. The

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first rust Avr protein identified, AvrL567 of the flax rust Melampsora lini, contains a predicted signal peptide, suggesting that it is secreted (Dodds et al., 2004). In the oomycetes, a specific conserved motif has been found in different secreted protein sequences (Ellis et al., 2006). Although many of our unisequences appear to encode a signal peptide, this “RxLR” motif was not observed in any of our unisequences. Furthermore, all of the unisequences containing signal peptides that were analyzed by quantitative PCR showed specific expression late in the infection process, not earlier. A phospholipase-related domain was encoded by Singleton14. Phospholipase proteins are secreted proteins, and in Candida albicans and Cryptococcus neoformans, phospholipase B protein has been identified as the virulence factor (Cox et al., 2001; Ghannoum, 2000). Because the expression of this unisequence was limited to 240 hai and rather low, the significance of the phospholipase domain is not clear. Contig50 was the most abundant sequence in our library, comprises of 431 ESTs. Contig11 was third in abundance, containing 63 sequences. Neither of these contigs showed similarity with protein or P. pachyrhizi sequences in NCBI databases. While they were highly represented in our library, each was expressed only at 240 hai. Transposable elements (TE) have been found and studied in filamentous fungi (Daboussy and Capy, 2003; Kempken and Kück, 1998). Our results show several unisequences (Contig9, Contig17, Contig27, Contig33 and Singleton60) with high homology to transposases in the Tc1/mariner family or containing stretches of sequence similar to a transposase Tc1/Tc3 domain. Since most of the similarity was with genes encoding TE from flies, plants and bacteria, we don’t know if these expressed genes were coming from the rust side or the plant side. We know that plant genome stability is affected by abiotic stresses (Lebel et al., 1993; Puchta et al., 1995) but there are only few evidences about it during biotic stresses. Lucht et al. (2002) found that there is an increase in somatic recombination frequency in Arabidopsis following the infection with the oomycete pathogen Peronospora parasitica. With these results, they were tempted to speculate that somatic recombination events stimulates by pathogen stress might be involved in the evolution of plant R-gene clusters and new pathogen specificities. Since only few soybean cultivars have resistance to SR isolates, the TEs identified in our experiment may play some role in the lack of resistance in soybean plants. A similar phenomenon was observed in rice, where Pot3 transposon introduced into the promoter of AVR-Pita, an avirulence gene of Magnaporthe grisea, caused the gain of virulence toward Yashiro-mochi, a rice cultivar containing the disease resistance gene Pi-ta (Kang et al., 2001).

3. Identification of soybean genes involved in response to SR infection 3.1 Methodology A microarray study was performed to identify soybean genes expressed in response to SR. Trifoliate leaves of soybean cv. Williams 82 inoculated with P. pachyrhizi isolate MS06-1 240 hai showed brown coloration in the mesophyll/palisade cells layer most of the time close to an uredinium but sometimes far from an uredinium (Figure 3C-D). Three million square microns of mesophyll/palisade cells showing this brown coloration were collected by LCM as well as 3,000,000 μm2 of noninfected mesophyll/palisade cells serving as control. RNA was extracted from each 1,000,000 μm2 of mesophyll/palisade cells (three replicates from each infected and noninfected samples) by using the same technique used to extracted RNA from uredinium-enriched samples. RNA was amplified to increase starting material. The

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GeneChip® Soybean Genome Array (Affymetrix®) was used for the microarray analyses. The array contains 37,744 G. max probe sets (35,611 transcripts). Details of the GeneChip® Soybean Genome Array can be obtained at (http://www.affymetrix.com/index.affx). 3.2 Results and discussion A total of 2,020 genes were found to be significantly differentially expressed (fold-changes of ≥ 2.0 and p-values ≤ 0.05 and a false-discovery rate of 5%) in mesophyll/palisade cells infected by SR, as compare to non-infected control plants. Out of the 2,020 differentially expressed genes, 380 were induced, and 1,640 were suppressed. There were 1,442 down-regulated genes identified that share similarity with genes encoding known proteins, while 60 down-regulated genes share similarity with unknown proteins and 138 genes share no similarity (e-value ≤ 10-2). All annotations were obtained by performing BLASTX from the Affymetrix ID accession available at http://affymetrix.com/index.affx. The fifty most down-regulated genes are listed in Table 2. Most of the down-regulated genes with similarity to genes encoding known proteins were included in the metabolism and energy functional categories (Figure 4A). During an infection, a lot of changes occur in the host plant tissues (Hoch, 1993). The first major change consists of a decrease in the rate of photosynthesis correlated with an increase in the area of infected tissues. When broad beans are infected by rust and sugar beets by powdery mildew (Farrar and Lewis, 1987), chloroplasts lose their structural integrity due to the pathogen infection. This change in the chloroplast ultrastructure corresponds to a reduction in the capacity for sucrose production. Early in the infection process of leaves by rust, the photosynthetic rate is unaltered; after sporulation there is a decrease in the ability of the plant to fix carbon, which seems to be related to the destruction of the chloroplasts, the degradation and the loss of chlorophyll concentration. The infection of the plant causes a block in the non-cyclic electron transport chain by reducing the amount of cytochromes without affecting the integrity of the photosystems I and II. Indeed, our results show that photosynthesis in soybean leaves is highly affected 240 hai by SR as well as carbon fixation. Numerous genes encoding enzymes involved in photosynthesis are suppressed compared to the uninfected plant including genes encoding chlorophyll A-B binding proteins (LHCA2, LHCB4.2, LHCB4.3, CAB, LHCA3.1, LHB1B1, LHB1B2, LHCB2.1, LHCB2.3, LHCB3, etc), photosystem I reaction center (subunit II, III, psaK, PSI-N, V, VI, XI) and photosystem II proteins, ferredoxin proteins, and fructosebisphosphate aldolase. These results are in agreement with Polesani et al. (2008), who studied transcriptome changes in grapevine infected by Plasmopara viticola causing mildew. The most striking transcriptional down-regulation in grape leaves infected with mildew was observed in genes involved in photosynthesis e.g. chlorophyll a-b binding proteins and photosystem components, consistent with a measurable reduction in chlorophyll content during pathogenesis. Transcriptional down-regulation of photosynthesis-related genes has been reported previously also during compatible interactions between potato and P. infestans (Moy et al., 2004) and between soybean and P. sojae (Restrepo et al., 2005). The second major change consists of the uncoupling of oxidative phosphorylation in the chloroplast. This uncoupling prevents ATP synthesis via the electron transfer chain and favors the accumulation of ADP and the increase in rate of oxygen uptake. This increase in the rate of respiration is probably due to the presence of respiring fungal tissues. Such increased respiratory levels lead to the rapid depletion of the plants carbohydrate reserves. Since there is less carbohydrate available for production of ATP molecules, an increase in

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Probe set Down-regulated Gma.10892.5.S1_at Gma.5294.1.S1_at Gma.1379.2.A1_at Gma.10892.1.S1_a_at Gma.1201.1.A1_at Gma.3304.1.S1_at Gma.11116.4.S1_at Gma.3241.1.S1_a_at Gma.11116.2.S1_at Gma.3208.2.S1_a_at Gma.15007.1.A1_s_at Gma.15091.2.S1_at Gma.10591.2.S1_at Gma.1355.2.S1_s_at Gma.4575.1.S1_at Gma.11254.2.S1_at Gma.5294.1.S1_s_at Gma.1992.1.S1_at Gma.3161.1.S1_at Gma.17554.1.S1_a_at Gma.4385.1.S1_s_at Gma.10620.1.S1_at Gma.10151.1.S1_at Gma.2224.1.S1_s_at Gma.10771.1.A1_a_at Gma.15376.1.A1_s_at Gma.1160.1.S1_at Gma.289.1.S1_s_at Gma.9720.1.S1_at Gma.2503.1.S1_at Gma.1955.4.S1_a_at Gma.11116.4.S1_s_at

Gene Annotation

Carbonic anhydrase gibberellin-regulated family protein Gonadotropin, beta chain; Gibberellin regulated protein Carbonic anhydrase Hypothetical protein Plant lipid transfer/seed storage/trypsinalpha amylase inhibitor Major intrinsic protein Germin-like protein Expressed protein Oxalic acid oxidase Ferredoxin [2Fe-2S], plant unknown protein Glycine cleavage system H protein, mitochondrial precursor ribosomal protein L17 family protein Fructose-bisphosphate aldolase SAH7 protein gibberellin-regulated family protein Hypothetical protein Glutamine synthetase Light harvesting chlorophyll a /b binding protein of PSII fasciclin-like arabinogalactan protein FLA2 NADH-dependent hydroxypyruvate reductase thylakoid soluble phosphoprotein Tubulin beta-1 chain thiamin biosynthesis protein Photosystem I reaction center subunit VI, chloroplast precursor Ferritin-3, chloroplast precursor Ribulose bisphosphate carboxylase small chains, chloroplast precursor Transketolase, C-terminal-like NADH-dependent hydroxypyruvate reductase Chloroplast oxygen-evolving enhancer protein Major intrinsic protein

Fold change

p Value (-10x)

-929,3 -624,9

-05 -04

-499,6

-07

-498,9 -475,5

-03 -04

-391,4

-05

-387,0 -363,6 -342,6 -241,5 -237,0 -232,6

-03 -04 -05 -05 -03 -04

-231,7

-05

-230,5 -226,4 -224,2 -214,0 -204,3 -187,3

-06 -05 -05 -03 -04 -03

-186,5

-04

-183,7

-04

-181,0

-04

-175,8 -175,6 -171,0

-04 -04 -05

-170,9

-07

168,545

-04

-161,4

-04

-155,6

-04

-151,0

-03

-148,4 -148,1

-03 -04

309

Soybean Rust: Five Years of Research

Probe set

Gma.10852.2.S1_at Gma.15830.1.S1_s_at Gma.5785.1.S1_at Gma.1791.1.S1_at Gma.11034.1.S1_at Gma.289.1.S1_x_at Gma.2360.1.S1_at Gma.15462.1.S1_a_at Gma.3317.2.S1_a_at Gma.2360.3.S1_at Gma.16678.1.S1_at Gma.16829.1.S1_x_at Gma.7309.2.S1_s_at Gma.15620.1.S1_at Gma.15814.1.A1_at Gma.10771.3.S1_x_at Gma.12822.1.S1_at Gma.14123.1.S1_at Up-regulated Gma.15636.2.S1_x_at Gma.3702.1.S1_at Gma.17733.1.S1_s_at Gma.6999.2.S1_s_at Gma.2821.1.S1_at Gma.6999.1.S1_s_at Gma.3734.1.S1_at Gma.5574.1.S1_s_at Gma.6999.1.S1_x_at Gma.3713.1.S1_s_at Gma.2593.1.S1_s_at Gma.772.1.S1_at Gma.5529.1.S1_at Gma.12045.1.S1_at

Gene Annotation Photosystem I reaction center subunit II, chloroplast precursor Expressed protein endo-1,4-beta-glucanase, / cellulase, putative Ferredoxin-B Oxygen-evolving enhancer protein 1, chloroplast precursor Ribulose bisphosphate carboxylase small chains, chloroplast precursor Light harvesting chlorophyll a /b binding protein of PSII RNA-binding region RNP-1 peroxiredoxin Q, putative Light harvesting chlorophyll a /b binding protein of PSII Chloroplast thioredoxin M-type Nodulin-26 Glycolate oxidase 50S ribosomal protein L12, chloroplast precursor Xyloglucan endotransglycosylase/hydrolase 16 protein thiamin biosynthesis protein cytochrome b6f complex subunit (petM), putative Hypothetical protein Hypothetical protein Endochitinase PR4 precursor Proteinase inhibitor I13, potato inhibitor I Stress-induced protein SAM22 Osmotin Stress-induced protein SAM22 Proteinase inhibitor I13, potato inhibitor I Pleiotropic drug resistance protein 12 Stress-induced protein SAM22 Aldo/keto reductase AKR Glutathione S-transferase GST 15 Hypothetical protein NAD(P)H-dependent 6'-deoxychalcone synthase Asparagine synthetase 1

Fold change

p Value (-10x)

-139,8

-03

-137,6 -135,8 -135,8

-04 -05 -05

-135,6

-02

-135,5

-05

-134,6

-05

-129,8 -128,7

-03 -02

-124,5

-03

-124,0 -124,0 -122,8

-06 -02 -02

-122,1

-04

-121,1

-03

-118,4

-06

-117,1

-04

-116,1

-04

621,4 492,6 458,2 438,6 416,8 409,0 366,6 335,8 220,7 183,8 182,2 143,1

-03 -05 -03 -02 -04 -02 -06 -06 -03 -04 -03 -03

133,8

-04

128,3

-04

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Probe set

Gene Annotation

Gma.9397.1.S1_at Gma.16547.1.S1_at Gma.6327.1.S1_s_at Gma.5950.1.S1_s_at Gma.10717.1.S1_a_at Gma.2096.1.S1_at Gma.3604.1.S1_at Gma.5709.1.S1_at Gma.9947.1.S1_at Gma.16778.1.S1_at Gma.744.1.S1_at Gma.4185.1.S1_at

NHL3 (NDR1/HIN1-like 3) WRKY86 B12D protein Dirigent protein integral membrane family protein Protein At2g29340 Caffeoyl-CoA O-methyltransferase 1 unknown protein Hypothetical protein VQ motif-containing protein WRKY transcription factor 41 ATEXLB1 (EXPANSIN-LIKE B1) Coatomer protein complex, beta prime; beta'COP protein Polyphenol oxidase Cytochrome P450 monooxygenase CYP82E13 disease resistance-responsive protein-related / dirigent protein-related thaumatin-like protein UVI1 alpha-hydroxynitrile lyase Cytosolic glutamine synthetase beta2 R 14 protein Vesicle-associated membrane protein 725 Aldehyde dehydrogenase [Vitis 2'-hydroxydihydrodaidzein reductase thaumatin-like protein LacZ protein Cytochrome P450 71D8 NAC domain protein NAC1 F1N19.23 F12P19.3 pyridine nucleotide-disulphide oxidoreductase family protein AMP-binding protein Transferase endo-1,3-beta-glucanase F17O7.4 No apical meristem (NAM) protein-like

Gma.1654.1.S1_s_at Gma.7559.1.S1_s_at Gma.16709.1.S1_s_at Gma.15568.1.S1_at Gma.2821.2.S1_a_at Gma.17802.1.S1_at Gma.17305.1.S1_at Gma.4375.1.S1_s_at Gma.2523.1.S1_s_at Gma.1537.1.S1_at Gma.8331.1.S1_at Gma.15664.1.S1_at Gma.2821.2.S1_at Gma.7728.1.S1_at Gma.8401.1.A1_at Gma.1748.1.S1_at Gma.17594.1.A1_at Gma.2773.2.S1_at Gma.5129.1.S1_at Gma.4483.1.S1_at Gma.17929.1.A1_at Gma.2578.1.S1_at Gma.12031.2.S1_x_at Gma.8113.1.A1_at

125,4 116,9 114,5 107,8 105,5 104,2 93,3 87,8 84,4 83,1 77,9 77,8

p Value (-10x) -04 -03 -04 -04 -03 -03 -05 -03 -05 -04 -04 -04

74,6

-03

72,6 67,8

-05 -05

66,9

-04

66,7 63,0 62,4 57,0 50,3 50,0 49,6 49,1 48,6 48,0 48,0 47,5 47,1 46,9

-04 -04 -04 -02 -05 -03 -05 -03 -03 -03 -02 -05 -04 -03

46,8

-03

46,3 45,7 45,4 45,2 44,9

-03 -04 -05 -04 -03

Fold change

Table 2. List of the fifty most greatly induced and suppressed annotated genes in soybean cv. Williams 82 10 dai by SR (pValue ≤ 0.05).

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311

pentose phosphate metabolism is needed. This is what is seen in some plant-fungal interactions such as rice infected by the sheath blight fungus, Rhizectonia solani (Danson et al., 2000). In this case, the PEP carboxylase is down-regulated. However in our experiment, we observed a massive decrease in the abundance of transcripts of genes important in the pentose phosphate metabolism as well as most of the other pathways of carbohydrate metabolism that produce ATP, including the tricarboxylic acid (TCA) cycle, starch and sucrose metabolism, galactose metabolism, inositol phosphate metabolism, glycolysis, and the pentose phosphate pathway. This shutdown was reflected by the down-regulation of many genes involved in carbohydrate metabolism as well as carbon fixation such as genes encoding ribulose-bisphosphate carboxylase, ribulose-phosphate 3-epimerase, transketolase, sedoheptulose-bisphosphatase, fructose-bisphosphate aldolase, triosephosphate isomerase, glyceraldehyde-3-phosphate dehydrogenase and phosphoglycerate kinase. The overlay of all significantly differentially expressed genes on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways has been used to facilitate results interpretation. PAICE, a tool for coloring KEGG pathways given Enzyme Commission (EC) accessions (http://paice.sourceforge.net) has been used to perform this analysis. This bioinformatics software was employed for its ability to handle duplicate gene-copies as well, color genes with large variances between time points, as well as color accessions given fold-change values. Figure 5 represents the pentose phosphate metabolism where enzymes corresponding to encoded genes expressed in our dataset have been colored depending on their expression level. Some of energy required by plants for respiration is probably coming from the conversion of lactate to pyruvate by lactate dehydrogenase. Pyruvate goes to the TCA cycle to produce ATP molecules needed. This alternative way of producing ATP molecules is highlighted in our study by the up-regulation of some enzymes involved in the pyruvate metabolism. Pentose phosphate metabolism is the main pathway for production of phenolic compounds, which are responsible for the activation of defense mechanisms. Suppression of expression of many genes encoding proteins involved in pentose phosphate pathway, including genes encoding transketolase, ribulose-phosphate 3-epimerase, ribose-phosphate pyrophosphokinase 1 and fructose-bisphosphate aldolase, present in our results suggest that pentose phosphate metabolism is down-regulated during SR infection which agrees with

Fig. 4. Functional categorization of the probe sets (a) down- (%) and (b) up-regulated (%) in soybean cultivar Williams 82 infected by SR 10dai.

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Fig. 5. Expression levels of genes encoding enzymes overlaid on the pentose phosphate pathway. The expression level for each gene encoding enzymes is associated with a specific color. Enzymes colored in red are down-regulated, the ones colored in green are upregulated (regular red or green when expression falls in plus or minus 25% the mean expression value range, dark red or green when expression is greater than 25% of the mean expression value and light red or green when expression is lower than 25% of the mean expression value), the ones colored in orange are extremely down-regulated while the ones colored in aqua are extremely up-regulated and finally the ones colored in yellow indicates that this specific gene encodes for different forms of the same enzyme and those different copies are differentially regulated. The color code is the same for fig.6. the fact that susceptible plant don’t succeed to build an efficient defense mechanism. However, we found that many genes encoding enzymes involved in the biosynthesis of phenylpropanoids are up-regulated including genes encoding caffeoyl-CoA 3-Omethyltransferase and chalcone synthase (Figure 6). Also, the gene encoding PEP carboxylase was up- regulated in our experiment; this enzyme can convert 3-dehydroshikimate to pcoumarate, which is the substrate driving the production of phenylpropanoid compounds. There were 316 up-regulated genes that shared similarity with genes encoding known proteins, while 15 genes shared similarity with unknown proteins and 49 genes were not similar to other genes (e-value ≤ 10-2). The fifty most highly induced genes are listed in Table 2. Most of the up-regulated genes with similarity to genes encoding known proteins were related to defense and disease and metabolism (Figure 4B). Some genes encoding proteins involved in plant defense were up-regulated, but no defense pathway appeared to be completely activated at the transcript level. Genes were induced that encode β-1,3-glucanase, glutathione S-transferase, thaumatin –like protein, osmotin, disease resistance-responsive proteins-related, stress-induced protein and the pathogenesisrelated protein 10 (PR-10). All are induced by salicylic acid (SA), which is produced in response to pathogen infection (Murphy et al., 2000; Narusaka et al., 1999). However, no genes encoding enzymes necessary for synthesis of SA were noted as induced. Other genes, such as those encoding polyphenol oxidase and cysteine protease inhibitor, which are induced following jasmonic acid (JA) synthesis (Thaler et al., 2001; Farmer et al., 1992), were also induced. Also, genes were enduced that encoded the enzymes 12-oxophytodienoate

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Fig. 6. Expression levels of genes encoding enzymes overlaid on the phenylpropanoid biosynthesis pathway. reductase and allene oxide synthase (AOS) that are involved in JA synthesis. However phospholipase A2 and lipoxygenase 2 were suppressed. A gene encoding chitinase IV was up-regulated as were genes encoding chitinase proteins. These are well known digestive enzymes that break down glycosidic bonds in chitin which comprise the cell wall of fungi. They are also induced following JA synthesis (Davis et al., 2002; Rakwal et al., 2004). However, induction of all these genes is not sufficient to elicit an effective defense response against SR in this susceptible soybean cultivar, but it is apparent that the plant continues to fight the infection process by expressing defense-related genes. One condition that contributes to the rust infection on grasses is low nitrogen levels in the soil (Doubrava and Blake, 1999; Hagan, 2005). By shutting down nitrogen metabolism, the fungus improves its ability to infect. Indeed, a decrease in nitrate reductase activity has been observed by Sadler and Scott (1974) during the first 2 days of infection of barley leaves by Erysiphe graminis. However, most studies report that nitrogen assimilation increases after this early stage of infection (Jenkin, 1977; Walters et al., 1984). van de Mortel et al. (2007) demonstrated that there is a biphasic change in mRNA accumulation in response to SR infection. There is an increase in levels of transcripts encoding enzymes involved in nitrogen metabolism after 24 hai, then a decrease at 10 dai. In our experiment, we also saw a decrease in expression of genes encoding enzymes involved in nitrogen metabolism including genes that encode nitrate reductase, nitrilase, glutamate synthase, glutamine synthase, and aminomethyltransferase. However, we found that transcripts encoding asparagine synthase, also involved in nitrogen metabolism, had a different expression profile at 10 dai, its expression increased. Asparagine synthase activity has been reported to increase in barley leaves infected by E. graminis at later stages of infection (Sadler and Scott, 1974). However, in barley other enzymes (NAD+ glutamate dehydrogenase, NADP+ glutamate dehydrogenase and glutamine synthase) involved in nitrogen assimilation were also increased. In our experiments with soybean, asparagine synthase expression could ultimately be used to produce alanine, a precursor of pyruvate which would allow the soybean leaves infected with SR to produce ATP via the TCA cycle. Many studies on biotrophic interaction between a fungus and its host plant show that translational activity as well as ribosomal biogenesis is reduced at the early stage of

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infection reflecting a major change in gene translation (Mould and Heath, 1999; Heath, 1997). Yamamoto et al. (1976) demonstrated that this reduction in protein synthesis is really specific to the early stage of infection up to 3 dai. After 3 dai, protein synthesis increases (Heinz et al., 1990). On the other hand, Polesani et al. (2008) showed at the latest stage of infection of grapevine with Plasmopara viticola, that genes involved in protein metabolism are predominantly repressed suggesting a repression of protein synthesis and turnover (Polesani et al., 2008). These results are in agreement with our data which show a downregulation of protein synthesis at 10 dai reflected by the down-regulation of many genes encoding ribosomal proteins such as the large subunit of the ribosomal protein L3 and the small subunit of the ribosomal protein S17. If we compare our study resulting from laser capture microdissected tissues to the study of van de Mortel et al. (2007) using whole leaves of a susceptible genotype at 7 dai, we find that out of our 380 up-regulated genes, 75 were identified in infected leaf tissue by van de Mortel et al (2007) as well, while 305 genes were unique, identified only in our experiment which focused on gene expression specifically at the infection site. All genes classified in our dataset as related to cell growth and division and intracellular traffic functional categories were unique. Eighty percent of genes we identified as members of the energy category were unique including phosphoenolpyruvate (PEP) carboxylase. About 75% of our genes included in cell structure, protein destination and transcription categories were unique to our experiment. Finally, 58% of our genes classified as involved in signal transduction were unique to our LCM isolated material. Out of 1,640 down-regulated genes identified in our experiment, 1,601 were unique as compared to the results of van de Mortel et al. (2007). Of the 39 genes in common with those identified by van de Mortel et al. (2007) were triosephosphate isomerase and transketolase involved in carbon fixation and many 50S ribosomal proteins. These results indicate that there are many different changes occurring between 7 and 10 dai in a susceptible soybean plant infected by SR. Some changes have already been seen in other fungi including other rusts but many changes seem to be specific to SR.

4. Deep sequencing assay to identify SR and soybean genes from infected plants Since 2004, many companies have been working in the area of next generation sequencing to provide high throughput and deep sequencing technology at low cost. In 2006 Solexa developed a next generation genetic analysis system. This system has been used for multiple applications rely on sample preparation. It allows DNA sequencing, transcriptome analysis and gene regulation analysis. RNA analysis through the mRNA-Seq assay to provide transcriptome analysis has been available since October, 2008. Since then, there have been numerous manuscripts describing the utilization of mRNA-Seq application on the Solexa platform to perform transcriptome analysis firstly focusing on mammals and yeasts, and more recently on plants. However, only few analyses have been done on infected plants. 4.1 Methodology We have performed an mRNA-Seq assay using the Solexa platform to test the potential offered by this new technology to identify more SR and soybean genes expressed under specific conditions as well as new genes from both organisms. Williams 82 soybean trifoliate leaves were collected at 240 hai and before inoculation (time-zero). Fragmented mRNA from

Soybean Rust: Five Years of Research

315

these samples was used as template for cDNA synthesis using a standard protocol. The Genomic DNA Sample Preparation Kit from Illumina was used to complete the cDNA library preparation. Four picomolar of cDNA at 240 hai and eight picomolar of cDNA at time-zero were hybridized to the flow cell surface in a cluster generation station (Illumina). The libraries were sequenced as 36-mers for 240 hai and 80-mers for time-zero on the Genome Analyzer IIx (GAIIx) (Illumina). Illumina Genome Analyzer pipeline v1.4 has been used to analyze the images generated by the GAIIx, to do the base-calling and to align reads onto a reference Soybean genome. In aligning of RNA-Seq reads to the Soybean reference genome, the Soybean (Glycine max) v5.0 genome build was utilized (www.phytozome.net/soybean). In relation to the Soybean v5.0 build used for read mapping, corresponding functional annotations were derived from the JGI Soybean ftp site (ftp://ftp.jgipsf.org/pub/JGI_data/phytozome/v5.0/Gmax). Mapping of reads and derivation of transcript abundance was made possible using TASE (Hosseini et al., 2010), an open-source read-counting tool designed for Solexa datasets. TASE enables the ability to calculate how many reads mapped onto genomic transcripts thereby providing a mode of transcript expression. The resulting number of reads have been normalized by divided the number of reads for a given gene in an experiment by the total number of annotated reads in the same experiment multiplied by a constant (100,000). Executing TASE was made possible using KOG (clusters of euKaryotic Orthologous Groups) homology-based annotations, Soybean build v5.0 mRNA transcript genomic start and end sites, and the Solexa dataset containing reads for all sequenced lanes. Output from TASE contained the genomic region for a given transcript (in a specific chromosome and lane), with the number of reads mapping to that given region. PAICE was used to overlay genes expressed on different metabolic pathways and color enzymes. 4.2 Results and discussion Solexa reads were aligned to the soybean genome using Phytozome version 5.0. Three categories of reads were identified. First were reads aligning to annotated regions of the soybean genome. Second were reads aligning to regions of the soybean genome wherein no annotation is present to indicate the presence of a gene. However, many of these unannotated regions appear to containing genes according to our data. Approximately 61 percent of the total number of reads (around 15 million reads) aligned to the soybean genome and fell into the first two categories. In spite of efforts to identify the function of all protein-coding loci, there are still unannotated regions of the genome that encode mRNA regions without any functional annotation. We refer to these regions as positionallyunannotated genes. Among all reads aligning to the soybean genome, an average of 54 percent mapped to positionally-annotated genes which represent over 18,000 protein-coding loci, while the remainder of these reads mapped to positions currently unannotated in Phytozome. Sequence homology searches of the positionally-unannotated genes using several different public databases such as NCBI and InterPro revealed that 70% of the sequences share similarity with genes found in other plant organisms. Approximately 30% of sequences remained unknown. The third category of reads contains those that did not align to the soybean genome. About 39 percent (around 10 million) of reads fall into this third category. These may represent soybean genes encoded in gaps in the present version of the soybean genome or they may be SR genes. Some of these reads could be positively identified as being SR genes, however,

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the sequence and assembly of the genome of SR is not available. Some of these reads had high identity with genes of other fungi. The majority of these reads did not have high homology to other fungal genes nor the limited number of known SR genes. We presume that many of these represent newly discovered SR genes. 4.2.1 Soybean gene expression profile To characterize the differential expression of soybean genes at 10 dai, we analyzed the tag count for a specific gene at 10 dai sample versus the tag count at time-zero which give us a fold-change. Forty-three percent of our positionally-annotated genes were down-regulated at 10dai while 31% were up-regulated. Twenty-six percent seemed to maintain the same expression level at 10 dai compared to time-zero. Figure 7 shows in which functional category down- and up-regulated genes are involved.

Fig. 7. Functional categorization of (A) down- and (B) up-regulated genes expressed in soybean Williams 82 10 dai with SR compare to time-zero. Compared to microarrays, deep sequencing technology allowed the identification of 17,363 more genes and changes in transcripts of genes encoding proteins of seventy-one more metabolic pathways. General trends seen from the microarray results were still observed using deep sequencing technology which means that more genes were down-regulated. They were mostly involved in carbohydrate metabolism and protein synthesis but they were also involved in nitrogen metabolism and carbon fixation. Fewer genes were up-regulated and they were mostly involved in general defense mechanisms. In the down-regulated pentose phosphate pathway, there more genes found expressed using deep sequencing (Fig.5B) than microarrays (Fig.5A). The same observation can be done for the up-regulated phenylpropanoid biosynthesis pathway (Fig.6) where more genes encoding enzymes were found expressed using deep sequencing (Fig.6B) than microarray analysis (Fig.6A). Finding more genes allowed confirming or refuting the regulation of many metabolic pathways observed with our microarray results and also allowed linking between pathways. While microarrays showed down-regulation of many genes encoding proteins involved in protein synthesis, our deep sequencing results showed that numerous genes encoding enzymes involved in almost all amino acid metabolic pathways were down-regulated as well. Most of the encoding genes involved in glycine, serine and threonine metabolism were down-regulated but the serine-glyoxylate transaminase, responsible for the conversion of

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317

glycine to serine to hydroxyl-pyruvate leading to the glyoxylate/dicarboxylate metabolism, was clearly up-regulated. Since the glyoxylate/dicarboxylate metabolism is one of the only pathways in carbohydrate metabolism found up-regulated, the up-regutation of this specific gene make sense. Glyoxylate may be used by malate synthase to produce (S)-malate and subsequently malate dehydrogenase would produce oxaloacetate, which could enter the pathway for pyruvate metabolism or vice versa. This latest observation supports the belief that pyruvate metabolism is truly up-regulated. Since glyoxytate can be produce by purine metabolism, genes encoding enzymes responsible for its production have been found expressed and up-regulated which refute microarray results indicating otherwise. Successful development of many rust and mildew infections requires an adequate and uninterrupted supply of nutrients from the host. Purines are nutrients needed for many metabolic events in addition to nucleic acid synthesis in eukaryote organisms. With pyrimidines, they are essential constituents of cytokinines (D’Agostino and Kieber, 1998). As cyclic monophosphates and as ADP-ribose they are also involved in signal transduction (Scheel, 1998). Moreover, they are involved in the synthesis of secondary products (Suzuki and Takahashi, 1977). An obligate biotroph which is unable to synthesize purines de novo, must obtain these nutrients from its host. Purines may be derived from the breakdown of host nucleic acids and some enzymes involved in purine salvage are present in both host and pathogen. In barley infected with powdery mildew (Butters et al., 1985), appressoria metabolize adenine and adenosine and converted these compounds mostly into inosine. Guanosine metabolism was more limited and hypoxanthine was neither absorbed nor metabolized. In a healthy plant, adenosine was converted into other purines, but inosine was not the major metabolite. In soybean infected with soybean rust, changes in purines metabolism were also observed. Genes responsible for guanine and xanthine production seemed to be up-regulated compare to those responsible for hypoxanthine and adenine production. Even if microarrays showed that genes encoding enzymes involved in terpenoid and steroid biosynthesis pathways were down-regulated, it did not showed any expression of genes encoding proteins involved in brassinosteroid biosynthesis. In our deep sequencing experiment, many genes encoding enzymes involved in this pathway were found as downregulated. Brassinosteroids (BRs) are a class of plant polyhydroxysteroids that are ubiquitously distributed in the plant kingdom. BRs are involved in many different cellular responses such as cell elongation, pollen tube growth, xylem differentiation, leaf epinasty, and root inhibition (Clouse and Sasse, 1998; Mandava, 1988). Their role in plant resistance to diverse environmental stresses has been confirmed in many studies (Ikekawa and Zhao, 1991, Kamuro and Takatsuto, 1991). The potential of BRs to enhance plant resistance specifically against fungal pathogen infection has been investigated as successful in many studies (Khripach et al., 1999, 2000). However, BRs have also been found as stimulant for fungal growth and disease progression (Krishna, 2003) which mean that protective or deprotective type of BRs activity depend on the concentration of BRs, where it is expressed and is connected with the different stimulating points of either the plant or the pathogen (Korableva et al., 2002). Since most of the gene family involved in BRs biosynthesis included up and down-regulated genes, it is hard from our experiment to know if this pathway was up or down-regulated. But the interest stills the same about the involvement of BRs in plant defense. Genes involved in transport facilitation were strongly down-regulated. These genes mostly encoded aquaporins. In many cases infection of plants by pathogens can lead to water stress.

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Soybean - Molecular Aspects of Breeding

In plants compatible to Phytphthora spp. there is a decline in stomatal conductance upon infection, which can cause water stress (Manter et al., 2007). Aquaporin encoding genes, responsible for water transport, occur as multiple isoforms. For example, Arabidopsis thaliana encodes 35 aquaporin homologues and maize encodes at least 31 aquaporin homologues (Luu and Maurel, 2005; Chaumont et al., 2001). This may explain the high number of aquaporin encoding genes found in our fifty most down-regulated genes. On the other hand, down-regulation of genes encoding aquaporins in soybean infected by soybean rust is similar to what has been reported in esca-infected grapevine where some aquaporin encoding genes were repressed at late stages of the infection (Letousey et al., 2010). Deep sequencing allowed the analysis of expression of genes encoding proteins associated with salicylic acid (SA) biosynthesis, which is involved in defense. Genes encoding chorismate mutase, anthranilate synthase, isochorismate synthase as well as phenylalanine ammonia-lyase were identified and expression analyzed. Some of these genes were not present on the soybean array while some other were not found expressed using microarray. Genes involved in other biosynthetic pathways closely related to defense were also upregulated such as genes encoding enzymes involved in jasmonic acid and ethylene production but in less abundance. Another set of up-regulated genes encoded enzymes involved in glycan metabolisms. In eukaryotic cells, both soluble and membrane proteins that enter the endoplasmic reticulum (ER) system may undergo a posttranslational modification called N-glycosylation. Nglycosylation occurs in two phases, namely, core glycosylation in the ER and glycan maturation in the Golgi apparatus (Kukuruzinska and Lennon, 1998; Helenius and Aebi, 2001). The biological functions of N-glycan modifications in the Golgi apparatus are well established in humans, insect and yeast (Sarkar et al., 2006; Bates et al., 2006). In plants, the first Arabidopsis thaliana mutant lacking complex N-glycans was reported in 1993 (von Schaewen et al., 1993). Plants with altered N-glycan modification pathways that are devoid of potentially immunogenic complex N-glycans are mostly used for the production of pharmaceutical proteins (Strasser et al., 2008; Koprivova et al., 2004) and could serve as potential food crops with reduced allergenicity. Until recently, however, complex plant Nglycans have not been associated with essential biological functions in their host plants due to lack of obvious phenotypes of mutant plants defective in complex N-glycan biosynthesis. Kang et al. (2008) recently reported that mutants defective in complex N-glycans show enhanced salt sensitivity, establishing that complex N-glycans are indispensable for certain biological functions. However, there is still no report on the involvement of N-glycans in response to biotic stresses. Our study showed that genes encoding enzymes involved in Nglycan biosynthesis as well as genes encoding enzymes involved in high-mannose type Nglycan biosynthesis were mostly up-regulated. Additional investigations on their role in plant-pathogen interaction need to be done. Genes involved in intracellular communication/signal transduction comprised 12% of the most up-regulated genes. These mostly included genes encoding serine/threonine protein kinases. The network of protein serine/threonine kinases in plant cells appears to act as a “central processor unit”, accepting input information from receptors that sense environmental conditions, phytohormones, and other external factors, and converting it into appropriate outputs such as changes in metabolism, gene expression, and cell growth and division (Hardie, 1999).Since these genes are involved in many biological processes, their presence in the most up-regulated genes was expected.

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4.2.2 New soybean genes The second category of reads are reads that align to the soybean genome on a position not yet annotated for presence of a gene or not yet annotated for functionality. This category includes about 5,500,000 reads. These reads were assembled into to get contigs using Velvet (Zerbino and Birney, 2008). About 12,000 contigs were built ranging from 30 to 801 nucleotides. Open reading frames were found for each of these contigs and used to find similarity against NCBI proteins database and Interpro database for conserved domains. Table 3 shows a subset of contigs not yet annotated but sharing similarity with gene encoding proteins, gene encoding conserved domains and/or microRNA (miRNA) from these different databases. Even if only few new genes shared similarity to genes directly related to defense, this additional information will be useful to complete annotation of the soybean genome sequence. Chromo Source

Contig Length (bp)

Chromo Location

Gm07

166

92109649310713

Gm11

249

1874648318786222

Gm14

327

61923226213926

201

40379464050174

Gm14 33696023374061

Gm15

Gm17

153

185

2448740624721204

Protein Description (Evalue) G. max unknown (-07) Glycine max proline-rich cell wall protein 3 (-77)

NA Vitis vinifera Vitis vinifera Top of Form hypothetical protein (-05) Arabidopsis thaliana Top of Form HIS4; DNA binding (-84) G. max SRC1 (E-49)

Conserved Domain

MiRNA (Evalue)

Signal peptide, Transmembran e regions

Physcomitrella patens miR414 (-05)

NA

NA Populus trichocarpamiR530b (-04)

NA Copper transport protein ATOX1related

NA

Histone H4

NA

Signal peptide, Transmembran e regions

NA

Table 3. Contigs displaying similarity to proteins, conserved domains and miRNA in the nonredundant protein NCBI, InterPro and miRNA databases. NA for none applicable. Since their discovery in 1993, miRNAs have been largely identified in eukaryotic organisms (Lee et al. 1993). They are post-transcriptional regulators playing multiple roles in negative regulation (transcript degradation and sequestering, translational suppression) and possible

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involvement in positive regulation (transcriptional and translational activation). By affecting gene regulation, miRNAs are likely to be involved in most biologic processes (Cuellar and McManus, 2005; Wilfred et al., 2007). In plants, miRNAs function to control tissue (leaf, root, stem, and flower) differentiation and development, phase switching from vegetative growth to reproductive growth, signal transduction, and the response to biotic and abiotic stress (e.g., salinity, drought, and pathogens) (Chen 2005; Zhang et al. 2006). There are 229 miRNA identified in soybean while there is about 1441 miRNAs identify in A. thaliana, 2641 in rice, 2780 in Populus trichocarpa, and 395 in Medicago truncatula (Zhang et al., 2010). Their identification is still important since they may play a role in plant defense. There was also one contig sharing similarity to a gene encoding cold-stress protein src1 from G. max itself. As it name tell us, the expression of this gene is activated early in response to temperature stress (Takahashi and Shimosaka, 1997). However, an increase in its transcript abundance had also been observed following drought, wounding and infection with soybean mosaic virus. Theis evidence showed that this gene may be involved in plant defense against pathogens. The expression of genes encoded by contigs aligning to the soybean genome on a position not yet associated with functionality and by contigs corresponding to potential new genes without similarity to known sequences, will be compared with that in A. thaliana to try to find information about their role in plant development and maybe plant defense. 4.2.3 Soybean rust gene expression The third category of reads are reads not aligning to the soybean genome included about 10,000,000 reads. These reads were assembled into contigs using Velvet. About 33,000 contigs have been built ranging from 50 to 1,991 nucleotides. An automated homology search against NCBI database found that only about seven percent of the contigs shared similarity to annotated genes. This result reflected the lack of information concerning filamentous fungi in general. Figure 8 shows with which functional category these genes have been associated.

Fig. 8. Functional categorization of soybean rust genes expressed in soybean Williams 82 10 dai.

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Metabolism and protein destination were the two functional categories where most of the annotated SR genes were included. In metabolism category, the amino acid metabolism and the carbohydrate metabolism were the most populated with genes. Many genes encoding enzymes involved in cysteine and methionine metabolism have been found including genes encoding S-adenosylmethionine decarboxylase, adenosylhomocysteinase and 5-methyltetrahydropteroyltriglutamatehomocysteine methyltransferase. Mutation of Fusarium graminearum cystathionine beta-lyase gene and methionine synthase gene involved in cysteine and methionine metabolism have been reported to reduce virulence of the fungus indicating that methionine synthesis is critical for plant infection (Seong et al., 2005). In Magnaporthe grisea, a methionine auxotrophic mutant was also reduced in virulence (Balhadere et al., 1999) and mutant defective for methionine synthase gene expression of Cryptococcus neoformans was nonpathogenic (Pascon et al., 2004). Auxotrophic mutants of other fungal pathogens defective in other amino acid metabolisms, such as histidine synthesis in M. grisea (Sweigard et al., 1998) and arginine synthesis in Fusarium oxysporum (Namiki et al., 2001), have also been reported to be reduced in virulence. Many genes encoding enzymes involved in carbohydrate metabolism have been identified. Some genes were encoding enzymes involved in glyoxylate metabolism such as malate dehydrogenase and malate synthase. Other genes were encoding enzymes involved in citrate cycle such as aconitate hydratase, succinate dehydrogenase, citrate synthase and isocitrate dehydrogenase. Presence of these enzymes as well as other involved in citrate cycle and glyoxylate metabolism in rust spores has been reported by Caltrider et al. (1963), Frear and Johnson (1961), and Staples and Stahmann (1964). This suggested that the TCA cycle and its glyoxylate shunt are present in urediniospores and are potentially very active. Small amounts of substrates which penetrate urediniospores membranes are rapidly converted via these pathways to the numerous products. In protein destination, we have found genes encoding proteins such as ubiquitinconjugating enzyme, ubiquitin-activating enzyme, proteasomes and different proteases. This kind of enzymes has been often found associated with the fungus germination and appressorium formation steps. Liu and Kolattukudy (1998) found an ubiquitin-conjugating enzyme induced by hard surface contact of Colletotrichum gloeosporioides conidia. Kim et al., (2004) were studying the proteome of Magnaporthe grisea and found that 20S proteasomes were present during appressorium formation. Fungal genes encoding proteases such as aspartyl proteases and serine proteases mostly belonging to the subtilisin class have been discovered to be expressed during infection (Mendgen and Deising, 1993; Carlile et al., 2000). Their role into pathogenicity is still circumstantial but there is some evidences suggesting a direct action of fungal proteases on host plant proteins from plasma membrane (Tseng and Mount,1974) or cell wall (Movahedi and Heale, 1990) for example, by degradation of plant cell wall proteins (Carlile et al., 2000; Dow et al., 1998;). Fungal proteases have been also found involved in the adhesion of fungal pathogens to host cells (Borg von Zepelin et al., 1999). Indirect evidence for a role of proteases in plant disease is that plants frequently react to microbial infections by the accumulation of fungal protease inhibitors (Chen et al., 1999; Vernekar et al., 1999). However, there is no mention in the literature about expression of fungal proteases late in the infection process but they also may be involved in degradation of cell wall proteins to allow expulsion of mature spores from the host in combination to turgor pressure.

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For the remaining 93% of the contigs, open reading frames have been found and used to manually find similarity against NCBI proteins database, Consortium for the Functional Genomics of Microbial Eukaryotes (COGEME) proteins database specific for phytopathogenic fungi and oomycetes and Interpro database for conserved domains. This additional analysis allow us to identify more genes sharing similarity to genes encoding proteins involved in many biological processes such as transcriptional control (LIM class homeodomain transcription factor, Lhx6/8 subclass from Branchiostoma florida), translation (translation elongation factor from Cryptococcus neoformans var. neoformans), lipids, fatty acid and isoprenoid biosynthesis (acetyl-CoA acetyl transferase and cyclopropane-fattyacyl-phospholipid synthase from Laccaria bicolor, fatty acid elongase from Saccharomyces cerevisiae), proteolysis and protein folding (dipeptidyl peptidase III from Neosartorya fischeri, FK506-binding protein 3 from Pyrenophora tritici-repentis, Hsp90 co-chaperone Cdc37 from Paracoccidioides brasiliensis) and glycolysis (mitochondrial pyruvate dehydrogenase E1 component beta subunit from L. bicolor). Genes sharing similarity to genes encoding protein involved in defense against oxidative stresses such as a catalase from Botryotinia fuckeliana and Taiwanofungus camphorates and a peroxidase from Talaromyces stipitatus and Gibberella zeae have also been found. Plants defend themselves against pathogen attack by invoking a myriad of mechanisms, including the hypersensitive response, the production of antimicrobial metabolites, cell-wall fortification and the papilla response. One of the most rapid responses to pathogen attack is the production of active oxygen species (AOS) at sites of invasion, named the oxidative burst (Zhang et al., 2004).On the other hand, very little is known about the effect of AOS and the response to AOS from the pathogen perspective. If AOS do have roles in plant defence then the evolution of antioxidant detoxifying systems may arm a pathogen with a major selective advantage in possible suppression of host defenses. But what may be the function of this detoxifying system at late stage of the infection still to be understood. During infection process, fungi face the challenges of recognizing susceptible hosts, overcoming physical and chemical barriers to invade plant tissues, withstanding host defense mechanisms, extracting nutrients for proliferation, and eventually disseminating to a new host. Morphological transitions during the infection process are an integral aspect of fungal phytopathogenesis. In this regard, the virulence arsenal of a fungal phytopathogen may include the ability to exhibit reversible switching between specialized morphological types of cells, the production of infection structures such as appressoria or haustoria, and the ability to produce spores as agents of dispersal. These morphological aspects of virulence are tightly regulated by environmental signals and signaling pathways that operate throughout infection. Fungal cells employ sophisticated signal transduction programs to sense and respond to specific environmental cues that drive changes in cell morphology and physiology. Signaling mechanisms that regulate fungal proliferation and differentiation include the highly conserved MAP kinase and cAMP (cyclic adenosine monophosphate) signaling cascades (Lee et al., 2002b). Probably part of the signaling pathway in SR, we also found an additional gene sharing high similarity to gene encoding a protein involved in intracellular communication and signal transduction, the serine/threonine protein phosphatase PP1 from Coprinopsis cinerea okayama. Genes sharing similarity to genes encoding proteins involved in the biogenesis of fungus cell wall have also been identified including genes encoding chitinase from Puccinia triticina and chitin deacetylase from Alternaria brassicicola.

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In addition, couple more genes encoding proteins containing a signal peptide have been pointed out.

5. Conclusion In this study, we focused on a susceptible interaction between one US commercial soybean cultivar and one US SR isolate. This specific interaction was chosen so that we could obtain enough material during the time-course to be able to study gene expression specifically in specific infection sites. In spite of obtaining a limited number of unisequences from the cDNA library sequencing, this experiment allowed us to identify a number of novel fungal mRNAs expressed in uredinia that are in some way associated with infection and/or proliferation. Some of the fungal genes from our cDNA library sequencing in addition to fungal genes from the deep sequencing assay may be further examined through gene silencing in soybean infected with P. pachyrhizi. Genes that contain signal peptides represent viable targets for gene silencing as well as genes encoding proteins involved in defense against oxidative stresses and biogenesis of cell wall to delay or even stop the infection process. Some of the soybean genes found expressed by our microarray assay represent interesting candidate genes for over-expression to control SR infection. For example, the reduction in nitrogen metabolism and protein synthesis that occurs in the plant at the end of the infection process may be an important trigger for sporulation of the fungus. Zuk et al. (2005) found that a repression of 14-3-3 proteins, well known for their interaction with other proteins, affect nitrogen fixation by regulating nitrate reductase (NR). Since NR is a key enzyme in nitrogen assimilation, they found that repression of these 14-3-3 proteins also boosts protein content. It may be possible to use 14-3-3 proteins to up-regulate instead of down-regulate nitrogen metabolism and protein synthesis at 10 dai with ASR and perhaps stop the fungus sporulation and its spread into the environment. Another example may be the overexpression of genes involved in purine metabolism. This may change the metabolite balance in favor of the plant and inhibit fungal sporulation. Overall, our experiment provides new information about the infection process both from the pathogen side and from the plant side. It gives us a better understanding of gene expression in the fungus and the plant during the life cycle and this may help us to identify approaches to broaden the resistance of soybean to SR. It provides insight into the needs of the pathogen and what genes are required and not needed for the pathogen to sporulate. Furthermore, our results show that the LCM procedure is a valid and powerful tool to collect specific infected portions of the plant for further examination. Thus, we can study gene expression patterns in a very specific infection structures, such as in the uredinium of SR, find genes essential to fungus development, and use those genes to try to build resistance into the plant. Moreover, our results using deep sequencing technology shows the performance of this quite new technology to increase our knowledge in a time effective manner. Finally, our study expands our knowledge about the development of this new exotic pathogen that threatens soybean production.

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15 Detection, Understanding and Control of Soybean Mosaic Virus Xiaoyan Cui1,2, Xin Chen1 and Aiming Wang2 1Jiangsu

2Agriculture

Academy of Agricultural Sciences, Nanjing, 21004 and Agri-Food Canada, 1391 Sandford Street, London, Ontario, N5V 4T3 1China 2Canada

1. Introduction Among 67 or so viruses that are able to infect soybean, 27 are considered a threat to the soybean industry (Tolin and Lacy, 2004; Saghai Maroof et al., 2008). Soybean mosaic virus (SMV) is the most prevalent virus and is recognized as the most serious, long-standing problem in many soybean producing areas in the world (Wang, 2009). SMV is a member of the genus Potyvirus in the family Potyviridae. The disease caused by SMV was first documented in the USA in 1915 by Clinton (1916) and SMV was named by Gardner and Kendrick (1921). Since then, the virus has been found in China, Japan, South Korea, Canada, Bazil, Australia and many other countries wherever soybean is grown. Infection by SMV usually results in severe yield losses and seed quality reduction. It has been reported that yield losses usually range from 8 to 50% under natural field conditions (Hill, 1999; Arif and Hassan, 2002) and reach up to 100% in severe outbreaks (Liao et al., 2002). Since SMV is a seed-borne viral pathogen and aphids can efficiently spread it from plant to plant while they feed, it is difficult to control the virus and produce SMV-free seeds. Furthermore, SMV often infects soybeans with other viruses such as Bean pod mottle virus (BPMV), Alfalfa mosaic virus (AMV) and Tobacco ringspot virus (TRSV) (Wang, 2009). Such synergistic infections with two or more viruses cause much more severe damages than infection by each virus alone (Hill et al., 2007; Wang, 2009). Utilization of soybean cultivars resistant to SMV is considered the most effective way of controlling the diseases. Extensive screening for soybean gemplasm resistant to SMV has resulted in the identification of three independent resistant genes, i.e., Rsv1, Rsv3, and Rsv4 (Hayes et al., 2000; Gunduz et al., 2002; Liao et al., 2002; Zheng et al., 2005; Li et al., 2010a). Interestingly, several naturally occurring resistance-breaking SMV isolates have also been reported that can break all three or two soybean resistance loci (Choi et al., 2005; Gagarinova et al., 2008a). The development of durable genetic resistance to SMV becomes a research priority for soybean breeders and soybean pathologists. This may depend on advances in the understanding of the SMV life cycle and molecular SMV-soybean interactions.

2. General biology of SMV 2.1 Physical and biological properties As a potyvirus, SMV virions consist of a capsid that is filamentous, flexuous rod-shaped with 650-760 nm in length and 15-18 nm in width (ICTVdB Management, 2006).

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Encapsulated in the viral particle is the viral genome which is a linear, positive-sense, single-stranded RNA molecule. The thermal inactivation point (10 min) of particles is 55 to 60°C. (Bos, 1972). The decimal exponent (DEX) of the dilution end point (DEP) is 10-3～10-5, and the longevity in vitro (LIV) is 2～5 days at 25°C (Hill, 1999). SMV is relatively stable at pH 6.0 and loses infectivity at pH values higher than 9 or lower than 4 (Galvez, 1963). The cylindrical, pinwheel-shaped inclusions formed by the viral CI protein, a characteristic cellular phenotype for potyvirus infection, are often found in the cytoplasm of infected cells (A. Wag, unpublished). 2.2 Host range and symptoms In comparison with other potyviruses, SMV has a relatively narrow host range. It infects six plant families, i.e., Fabaceae (also Leguminosa), Amaranthaceae, Chenopodiaceae, Passifloraceae, Schropulariaceae and Solanaceae, but mostly the Leguminosae including soybean and its wild relatives (Galvez, 1963; Hill, 1999). The symptoms induced by SMV depend on host genotype, virus strain, plant age at infection, and environment. In SMV-infected soybeans, symptoms commonly observed include rugosity, dark green vein banding and light green interveinal areas, stunting, leaf curling and seed coat mottling, male sterility, flower deformation, less pubescent, necrosis, sometimes necrotic local lesions, systemic necrosis and bud blight (ICTVdB Management, 2006). Some of these SMV symptoms may be masked at temperatures above 30°C (Hill, 1999). 2.3 Transmission Up to 30% or more of the seeds from SMV-infected soybean plants carry SMV depending on cultivar and time of infection before flowering (Bos, 1992). SMV-infected seeds are the primary inoculum source, though weeds and other plants may also serve as a reservoir of SMV. Further spread within and among soybean fields is through the activity of more than 32 different aphid species of 15 different genera in a non-persistent manner (Cho and Goodman, 1982; Arif and Hassan, 2002; Steinlage et al., 2002). Some aphid species are Acyrthosiphom pisum, Aphis craccivora, A. fabae, A. glycine, A gossypii, Myzus persicae, Rhopalosiphum maidis and R. padi. In addition, SMV can be efficiently sap-transmitted with or without the use of abrasives. 2.4 Strains Numerous SMV isolates have been reported worldwide. In the United States, at least 98 isolates of SMV have been documented (Cho and Goodman, 1979, 1982, 1983). Based on their differential reactions in two susceptible, i.e., Clark and Rampage, and six resistant soybean cultivars including Buffalo, Davis, Kwangyo, Marshall, Ogden and York, SMV isolates were classified into seven distinct strain groups, G1 through G7 (Cho and Goodman, 1979). Later, two more groups, G7A and C14 were added (Buzzell and Tu, 1984; Lim, 1985). Similarly, five strains (A to E) have been identified in Japan (Takahashi et al., 1963; Takahashi et al., 1980). In Canada, a necrotic strain, SMV-N, and a number of G2 isolates were identified (Tu and Buzzell, 1987; Gagarinova et al., 2008a; Viel et al., 2009). But SMV-N shares high sequence similarity with the G2 group and is thus considered a G2 isolate (Gagarinova et al., 2008b). In South Korea, SMV was also monitored by pathotype. All the SMV G strains, including G1 through G7, SMV-N, G5H, G7a and G7H, have been found (Seo et al., 2009), but dominant

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strains varied in different times. For instance, G5 caused about 80% of the SMV damages in the early 1980s, whereas in the late 1980s, G5H was a dominant strain, responsible for over 65% of the SMV-caused losses (Cho et al., 1983; Kim, 2003). More recently, G7H became the most prevalent strain and accounted for approximately 50% of SMV incidence in soybean fields (Kim et al., 2003; Seo et al., 2009). Due to the genetic variability of SMV and strong selection pressure, resistance breaking isolates may occur. Indeed, several resistance-breaking isolates including CN18 were identified in soybean fields in South Korea (Choi et al., 2005). In China, SMV isolates were grouped into strains based on geographical distributions and responses in soybean resistant cultivars (Wang et al., 2003; Guo et al., 2005; Wang et al., 2005; Zhan et al., 2006). For instance, SMV isolates found in Northeast China were grouped into three strains, No. 1, 2, 3 using three soybean cultivars (Lv et al.,1985), whereas SMV isolates identified in Jiangsu Province were classified into strains Sa, Sb, Sc, Se, Sg and Sh based on pathogenicity in 10 other cultivars (Pu et al., 1982; Chen et al., 1986). Recently, a more comprehensive study using 10 soybean cultivars has classified 606 SMV isolates identified in South China into 21 strains, SC1 through SC21 (Li et al., 2010b). Since an SMV isolate may be grouped into different groups when different sets of soybean cultivars are used, obviously this creates difficulty for SMV identification, exchange of resistance germplasm, and comparison of resistance assay results. It is necessary to establish a standardized system for strain classification in China as well as other countries in the world. It is worth to mention that SMV grouping based on their sequence similarity may be a more reliable approach. As more and more SMV isolates are sequenced, the complete genome sequences and partial gene sequences can be used to study the phylogenetic relationship and molecular variability (Frenkel et al., 1989; Saruta et al., 2005; Schirmer et al., 2005; Wetzel et al., 2006; Gagarinova et al., 2008b; Seo et al., 2009). The outcomes of these studies will certainly provide new insights into the evolutionary process of SMV in relation to its natural hosts. 2.5 Diagnosis Because the initial infection source of SMV is mainly the seeds from SMV-infected soybean plants, the accurate diagnosis of SMV is very important for its effective management. A conventional method for SMV detection is to inoculate diagnostic hosts (indicator plants) to observe differential visual symptoms and determine the host range. The diagnostic host species and symptoms for SMV are: Chenopodium album or C. quinoa, chlorotic local lesions; Lablab purpureus, necrotic local lesion; Macroptilium lathyroides, systemic mosaic; Phaseolus vulgaris, systemic mosaic with some strains in some cultivars, but often latent or no infection; P. vulgaris cv. Top Crop, necrotic local lesions at 30°C in detached leaves (ICTVdB Management, 2006). The advantage of this method is relatively simple and does not require expensive instrument and complex techniques. This method, however, is not sensitive and requires considerate time and greenhouse space. To overcome these shortcomings, serological assays have been used for SMV detection. These include the direct double sandwich – enzyme-linked immunosorbent assay (DASELISA), indirect-ELISA, tissue-print immunoassay (TPIA), dot immunobinding assay (DIBA), immunosorbent electron microscopy (ISEM), immunofluorescence and western blotting. Some of these methods, particularly ELISA-related technology, can be used for the detection and quantitative estimation of a large number of soybean samples and thus are commonly used for SMV detection and screening. In addition to protein-based technology, highly sensitive polymerase chain reaction-based technology such as reverse transcription polymerase chain reaction (RT-PCR) and real-time PCR have also been widely used for SMV

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detection. The SMV viral genome-derived PCR products may be sequenced, allowing comparison analysis with the published SMV sequence and providing the most accurate information for identification of virus strains.

3. Molecular biology of SMV 3.1 Sequence and genomic organization The genome of SMV is a positive single-stranded [(+)ss] monopartite RNA molecule. Complete sequencing of 45 SMV isolates including all G1 to G7 strains suggests the SMV genome is approximately 9600 nucleotides (nt) in length (Jayaram et al., 1992; Gagarinova et al., 2008a). There is a virus genome-linked protein (VPg) at the 5' end and a poly(A) tail at the 3' end. The genomic RNA contains one long open reading frame (ORF) and a smaller ORF resulting from a frame-shift, both together encoding 11 mature viral proteins (Berger et al., 2005; Chung et al., 2008). From the N to C terminus of the two polyproteins, the 11 mature proteins are: P1 (the first protein), HC-Pro (the helper component/protease), P3 (the third protein), P3N-PIPO (resulting from the frame-shift in the P3 cistron), 6K1 (the first 6 kDa peptide), CI (the cylindrical inclusion protein), 6K2 (the second 6 kDa peptide), NIaVPg (nuclear inclusion “a”–viral genome-linked protein; also VPg), NIa-Pro (nuclear inclusion “a” protein–the protease), NIb (the nuclear inclusion “b” protein), CP (coat protein) (Jayaram et al., 1992). Most of these viral proteins are multi-functional. 3.2 SMV-host interactions Due to the availability of a large number of SMV isolates as well as numerous soybean cultivars, the SMV-soybean interaction may be the most complex of virus-plant interactions. As briefly discussed above, SMV is classified into different strains based on their different responses on several susceptible and resistant cultivars. These responses include susceptible (mosaic or crinkling), necrotic, or resistance (symptomless). In soybeans, three independent, dominant resistance loci, Rsv1, Rsv3 and Rsv4 conferring genetic resistance to partial or all SMV strains have been identified. Rsv2 was initially assigned to the resistance gene in cultivar OX670 and later dropped when it was revealed to actually possess two resistance genes, Rsv1 and Rsv3 (Gunduz et al., 2001). Rsv1 is a single-locus, multi-allelic gene. It was initially named Rsv and found in PI96983 soybean (Kiihl and Hartwig, 1979) and later renamed Rsv1 (Chen et al., 1991). Subsequent studies on soybean cultivars resistant to SMV revealed additional eight different Rsv1 alleles, Rsv1-t, Rsv1-y, Rsv1-m, Rsv1-k, Rsv1-r, Rsv1-h, Rsv1-s and Rsv1-n from cultivars Ogden, York, Marshall, Kwanggyo, ‘Raiden’, ‘Suweon 97’, LR1 and PI507389, respectively (Kiihl and Hartwig, 1979; Buss et al., 1997; Buzzell and Tu, 1989; Chen et al., 1991, 1993, 1994, 2001, 2002; Ma et al., 1995; Zheng et al., 2005). The responses of soybeans carrying these Rsv1 alleles to different SMV strains are diverse, ranging from extreme resistance and necrosis to mosaic symptoms. The nine Rsv1 alleles confer resistance to lower numbered SMV strain groups (G1 to G3), but allow mosaic or necrotic reactions to higher numbered strain groups (G5 to G7). Rsv1 was mapped to the molecular linkage group F (soybean chromosome 13) in a cluster of resistance genes (Gore et al., 2002; Hwang et al., 2006). Rsv3, another single dominant gene, was found in cultivar Columbia, conditioning systemic necrosis against SMV strains G1 and G4 (Buzzell and Tu, 1989). Resistance cultivar L29, a Williams isoline contains an Rsv3 allele derived from cultivar Hardee (Gunduz et al., 2002). Soybean plants carrying Rsv3 alleles are resistant to higher

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numbered strains G5 through G7, but susceptible to lower numbered strains G1 to G4 (Gunduz et al., 2002). Rsv3 was mapped to the molecular linkage group B2 (soybean chromosome 14), also containing a cluster of disease resistance genes (Jeong et al., 2002). Rsv4 is the third resistance gene of soybean, independent of Rsv1 and Rsv3. It was reported in PI486355 and isolated in V94-5152, derived form hybrid PI486355 x Essex (Buss et al., 1997). Columbia was also found to carry Rsv4 (in addition to Rsv3) (Ma et al., 2002). Rsv4 is dominant, non-necrotic and non-strain specific, conferring resistance to all strains of SMV (Ma et al., 1995). It was mapped to the molecular linkage group D1b (soybean chromosome 2) where no other resistance genes have been found (Hayes et al., 2000; Saghai Maroof et al., 2010). To study Rsv1-SMV interaction, Hajmorad et al. (2005) used the Rsv1 gene in soybean PI96983 conferring extreme resistance to several G strains but susceptible to two G7 isolates, G7 and G7d. The former induced a leathal systemic hypersensitive response (LSHR) in PI96983, whereas the later, an experimentally evolved variant of G7, caused systemic mosaic symptoms. Through SMV chimeric infectious clones resulting from swapping different genomic regions of G7 and G7d and further SMV mutants through point mutagenesis, P3 was narrowed down to be the elicitor of Rsv1-mediated LSHR (Hajimorad et al., 2005). However, further studies using G7 and other SMV strains suggest the absence of P3 elicitor function alone is not sufficient to gain virulence and HC-Pro complementation of P3 is required for G7 to gain virulence (Hajimorad et al., 2006, 2008). To elucidate SMV-Rsv3 interaction, Zhang et al. (2009) used a G2 isolate (SMV-N) that systemically infects Rsv3 soybean and a G7 isolate which is restricted by Rsv3 (Zhang et al., 2009). Infection test using recombinant SMVs from exchanging fragments between the avirulent G7 and the virulent SMV-N concluded that both the N- and C-terminal regions of the CI protein are required for Rsv3-mediated resistance (Zhang et al., 2009). For G2-Rsv4 interaction, Chowda-Reddy et al. (2010) constructed two infectious clones corresponding to a naturally occurring resistancebreaking isolate and its closely related non-resistance-breaking avirulent isolate (Gagarinova et al., 2008a). Using the similar strategy described above, they determined that P3 in the G2 strain is an avirulent elicitor for Rsv4 (Chowda-Reddy et al., 2010). Despite these studies, how these resistance genes control resistance to SMV and how the elicitors trigger resistance response to SMV remain unknown. In susceptible cultivars, SMV-soybean interaction is a compatible reaction. After entry into the cell, SMV proceeds viral genome translation and replication, vial particle assembling, and cell-to-cell and long-distance movement. Such compatible virus infection often induces and suppresses host gene expression at the global level (Whitham et al., 2006). Babu et al. (2008) assessed transcriptional changes in susceptible soybean cultivar Williams 82 infected by SMV using microarray. A number of transcripts encoding proteins for hormone metabolism, cell-wall biogenesis, chloroplast functions and photosynthesis were shown to be repressed at 14 days post infection (Babu et al., 2008). Theses changes were associated with the highest levels of SMV genomic RNA in the host cells and the progression of mosaic and vein clearing symptoms (Babu et al., 2008). The expression levels of a number of transcripts corresponding to genes involved in defense were either downregulated or not affected at the early stages of infection, but upregulated at the late stages (Babu et al., 2008). These data suggest that in susceptible cultivars, the plant immune response is not activated until the late time point of infection and such a delayed defense response may be critical for SMV to establish its systemic infection.

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3.3 SMV replication SMV enters soybean cells either through a mechanical abrasion or by an aphid vector. Subsequently, uncoating of the viral RNAs in the cytoplasm, translation of the polyprotein and replication of the viral genome occur. Accumulated evidence suggests the replication of eukaryotic positive-strand RNA viruses including plant potyviruses is associated with intracellular membranous structures (Wei and Wang, 2008). These membranous vesicles have been proposed to provide a scaffold for anchoring the virus replication complex (VRC), confine the process of RNA replication to a specific safeguarded cytoplasmic location to prevent the activation of host defense responses, and to recruit the components required for replication and maintain the proper concentrations of these components (Wileman, 2006). The potyvirus VRC-containing vesicles seem to originate at endoplasmic reticulum exit sites (ERES), traffic along the microfilaments and target chloroplasts for replication (Wei et al., 2010). It has been shown that in potyvirus-infected plant cells, these vesicular compartments contain a number of host proteins, i.e., heat shock cognate 70-3 (Hsc70-3), poly(A)-binding protein (PABP), eEF1A and eIF(iso)4E, and several non-structural viral proteins, such as the 6K2, NIb (the viral RNA dependent RNA polymerase, RdRp), NIa (including NIa-VPg or NIa-Pro or as a precursor protein) and CI (Dufresne et al., 2008; Thivierge et al., 2008; Cotton et al., 2009; Wei et al., 2010a). The 6K2 (also called 6K) protein is an integral membrane protein that induces the formation of 6K-containing membranous vesicles at ERES (Wei and Wang, 2008). These ER-derived vesicles further target chloroplasts where they amalgamate and induce chloroplast membrane invaginations (Wei and Wang, 2008; Wei et al., 2010a). Thus, 6K plays a pivotal role in the formation and targeting of VRC-associated vesicles. Since NIb has the RNA polymerase activity (Hong and Hunt, 1996), NIa-VPg (also called VPg) is the genome-linked protein (Murphy et al., 1996; Schaad et al., 1996) and CI has the helicase activity (Laín et al., 1990), it is not surprising that these viral proteins are present in VRC-associated vesicles and involved in the replication of potyviruses (Revers et al., 1999). It has been suggested that NIa is brought into VRCs by the domain for the 6K protein present on the intermediate precursor protein 6K-NIa (Restrepo-Hartwig and Carrington, 1992; Thivierge et al., 2008). The NIb protein (RdRP) has been reported to be present in the vesicles induced by 6K-NIa due to its physical interaction with NIa or as an intermediate precursor (6K-NIa-NIb) (Daròs et al., 1999; Dufresne et al., 2008; Thivierge et al., 2008). The viral RNA is probably enclosed to VRCs by the RNA-binding domain of NIb (Thivierge et al., 2008; Dufresne et al., 2008). The host proteins, heat shock cognate 70-3 (Hsc70-3), poly(A)-binding protein(PABP) and eEF1A are recruited by VRC likely through interactions with NIb, and eIF(iso)4E is present in the replication vesicles as an interactor with VPg (Dufresne et al., 2008; Thivierge et al., 2008). Within the induced vesicles, the RdRp binds to the 3' termini of the viral (+)RNA to initiate transcription of negative strand replicative form RNA. This (-)RNA intermediate is used as a template to produce progeny (+)RNAs that are delivered to the cytoplasm for translation or encapsidation. The CP binds positive-sense progeny (+)RNAs to form progeny virions with VPg attaching on the end. The potyvirus replication possibly begins with uridylylation of VPg which acts as a primer for progeny RNA synthesis, a process shared by the Picornaviridae family (Puustinen and Mäkinen, 2004). 3.4 SMV cell-to-cell and long-distance movement After replication and assembling, nascent virus particles will spread to neighboring cells and further to other parts of the plant. The first process is called cell-to-cell movement and

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the latter long-distance movement. Cell-to-cell movement of viruses occurs through plasmodesmata (PD), a specialized intercellular organelle, unique to the plant kingdom. This is an active process mediated by virus-encoded protein(s) termed movement protein (MP). Potyviruses do not encode a dedicated MP, and movement functions have been allocated to several proteins. Of the 11 potyvrial proteins, CP, VPg, HC-Pro, CI and P3NPIPO have been suggested to have functions in intercellular transport (Dolja et al., 1994, 1995; Nicolas et al., 1997; Rojas et al., 1997; Carrington et al., 1998; Wei et al., 2010b; Wen et al., 2010). Accumulating evidence indicates that HC-Pro and VPg are essential in other aspects of the infection process such as viral genome replication or suppression on host defense (RNA silencing) (Kasschau and Carrington, 1998; Puustinen and Mäkinen, 2004), whereas CP, CI and P3N-PIPO are likely to be MPs of potyviruses. The potyviral CP is a three-domain protein with variable N- and C-terminal regions exposed on the particle surface and a conserved core domain that interacts with viral RNA (Allison et al. 1985; Shukla and Ward 1988). The C terminal part of SMV CP contains two small regions (amino acids 190-212 and 245-249) required for CP-CP interaction and virus assembly (Kang et al., 2006). Mutations in the CPcore domain result in defective cell-to-cell movement and virion assembly (Dolja et al., 1994, 1995; Rojas et al., 1997; Jagadish et al. 1993), suggesting potyviruses likely move as virions. The potyvirus CI has been suggested to play a role in cell to cell movement (Laín et al. 1990; Eagles et al. 1994; Klein et al. 1994). High-resolution ultrastructural analyses indicate that CI forms the cone-shaped structures at the cell periphery adjacent to PD (Rodríguez-Cerezo et al. 1997; Roberts et al. 1998, 2003). In the case of the newly found P3N-PIPO protein, it has been shown that mutation of the putative SMV PIPO domain impeded cell-to-cell movement (Wen et al.,2010) and that P3N-PIPO mediated the formation of CI conical structures at PD (Wei et al., 2010b). Based on the model suggested by Wei et al. (2010b), cell-to-cell movement may be initiated when the recruitment of nascent virus particles by CI or self-interacting CI structures at membrane-bound sites of replication adjacent to chloroplasts. Then CI-virion complexes may associate with either pre-targeted P3N-PIPO followed by trafficking to PD, or with PDassociated P3N-PIPO. CI structures accumulate from P3N-PIPO-anchored sites at PD, forming thread-like structures that might recruit additional virus particles for transport. Virus particles fed through the CI structures and PD to enter the adjacent cell may be facilitated by PDtraversing CI complexes. Long-distance movement is the process by which the virus moves from the mesophyll via bundle sheath cells, phloem parenchyma, and companion cells into phloem sieve elements (SE) where they are translocated, then unloaded at a remote site from which further infection will occur (Carrington et al. 1996). Due to the complexity of the various cellular structures involved, the molecular mechanism of potyviral long-distance movement is poorly understood. Four potyviral proteins, i.e., CP, HC-Pro, VPg and 6K2, have been suggested to be implicated in long distance movement. HC-Pro and CP are both multifunctional proteins (Shukla et al., 1994; Mahajan et al., 1996; Maia et al., 1996). The central region of HC-Pro and both termini of CP were shown to be essential for virus longdistance movement (Dolja et al., 1994, 1995; Cronin et al., 1995; López-Moya and Pirone, 1998). VPg affecting long-distance movement may function through its direct or indirect interaction with host components (Schaad et al., 1997). The 6K2 protein of Potato virus A is a host-specific determinant for long-distance movement in N. tabacum and N. benthamiana (Spetz and Valkonen, 2004). Potyvirus long-distance movement also requires a set of host factors. Strain and virus specific restriction of movement in plants has been described in several virus-host systems. Genetic characterization from natural ecotype variations and

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chemically induced mutants has revealed that at least three dominant genes named RTM1, RTM2 and RTM3 are involved in the restriction of long-distance movement of potyviruses in the Arabidopsis accession Columbia (Col-0) (Mahajan et al., 1998; Whitham et al., 1999). These RTM factors affect the potyvirus long-distance transport through direct or indirect interaction with CP (Decroocq et al., 2009).

4. Control of SMV The best strategy against plant viruses is either through the physical separation of the pathogen and host to avoid, or through the deployment of genetic resistance to prevent or limit the extent of the infection (Maule et al., 2007). In practice, the SMV-infected seeds and aphid transmission are the most prevalent factors causing SMV in fields. Thus, utilization of virus-free seeds and avoiding aphid transmission are an effective management measure against SMV in farming practices. Although considerable time and cost may be required for developing varieties, breeding for genetic stable varieties with the appropriate range of resistances is still the preferred and reliable approach to control the disease. 4.1 Breeding using natural resistance genes All the three genetically identified resistance genes (Rsv1, Rsv3 and Rsv4) have been deployed in China, the United States, Canada and other courtiers for controlling SMV. A number of soybean accessions (germplasm) and cultivars carrying resistance to SMV have been identified and used in the breeding program. V94-5152 confers an early resistance at the Rsv4 locus to SMV G1 to G7. The OX670, Tousan 140 and Hourei soybean were shown to possess two genes, Rsv1 and Rsv3 (Gunduz et al. 2001, 2002), conferring resistance to G1 to G7 too. In China, Zao18 and J05 also carry Rsv1 and Rsv3 (Liao et al. 2002; Zheng et al., 2006). Zao18 was reported to be resistant to all strains found in Northeast China and majority strains in Southern China, while J05 is resistant to the most virulent strains of SMV in Northeastern China (Zheng et al., 2000). In addition, Columbia soybean was shown to carry two genes, Rsv3 and Rsv4, for resistance to SMV G1 to G3 and G5 to G7 (Ma et al. 2002, 2004). All these soybean genotypes resistant to SMV are the valuable resource for breeding programs. Since the seed-borne, aphid-transmitted SMV is genetically variable and continually evolving via RNA recombination and spontaneous mutations by its own errorprone RdRp, strong directional selection would lead to the occurrence of resistance-breaking isolates (Choi et al., 2005; Saruta et al., 2005; Gagarinova et al., 2008a). Incorporation of multiple resistance genes into a soybean genotype (cultivar or variety) through gene pyramiding becomes a priority for soybean breeders to develop durable resistance to SMV. To assist in breeding programs, molecular markers of all the three resistance genes have been developed based on fine mapping with several molecular techniques such as restriction fragment length polymorphism (RFLP), random amplified polymorphism DNA (RAPD), amplified fragment length polymorphism (AFLP), simple sequence repeats (SSR) and single nucleotide polymorphisms (SNP) (Yu et al., 1994; Hayes et al., 2000; Gore et al., 2002; Jeong et al., 2002; Jeong and Saghai Maroof, 2004; Hwang et al., 2006; Shi et al., 2008). 4.2 Development of genetic resistance through biotechnology Pathogen-derived resistance (PDR) is an established, effective approach to engineer resistance to plant viruses in plants. It has been used to develop genetic resistance against a

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wide range of plant viruses including potyviruses (Powell-Abel et al., 1986; Lius et al., 1995; Di et al., 1996; Wang et al., 2009). This approach requires generation of transgenic plants with partial viral genomes. The resulting resistance is often mediated by RNA silencing or posttranscriptional gene silencing (PTGS) which induces sequence-specific degradation of viral RNA (Waterhouse et al., 2001). But the RNA silencing-mediated resistance may be overcome in two scenarios (Huang et al., 2010), i.e., in mixed infections by a strong silencing suppressor from unrelated viruses (Mitter et al., 2001) or through mutations during virus replication by the viral RdRP that lacks proofreading activities (Kang et al., 2005b). To develop PDR against SMV, the CP and 3’-UTR region of the SMV N isolate (strain G2) was engineered into soybean (Wang et al., 2001). The expression of the single copy of the partial viral genome segment was controlled by the cauliflower mosaic virus 35S promoter. Two transgenic lines showed high resistance to all SMV strains or isolates tested including G2, G6, G7 and an isolate named A15 obtained from South Carolina (Wang et al., 2001). None of the transgenic lines showed immunity to SMV infection (Steinlage et al., 2002). Since the transgenes that produce mRNA containing an intron-spanned hairpin structure usually induce high level of PTGS (Smith et al., 2000; Waterhouse et al., 2001), it is expected that soybean transformed with such constructs may obtain stronger resistance or immunity to SMV. 4.3 Novel strategies to control SMV During the past several years, several new strategies have been developed against plant viruses. At the protein level, one approach is to engineer transgenic plants producing desirable proteins that can inhibit activities of essential viral proteins (Sanfaçon, 2009). For instance, transgenic plants expressing single-chain antibodies specific for the viral RdRp were shown to be resistant to a tombusvirus and several related viruses (Boonrod et al., 2004). Broad application of this approach may be hindered by the adverse pleiotropic effects of the antibodies and low levels of protein accumulation due to unwanted PTGS (Sanfaçon, 2010). At the RNA level, one of the reported approaches is to utilize artificial miRNAs which are small RNA molecules of 21-25 nucleotides long and negatively regulate the expression of their target genes in plants. miRNA precursors can be modified to produce artificial miRNA specifically targeting virus of interest and to induce resistance to the virus (Niu et al., 2006). Artificial miRNAs may be designed to target conserved regions of a virus family or related viruses to gain broad resistance. Since the miRNAs approach eliminates the potentially undesired recombination events due to their short length and does not involve translation because of their untranslatable nature, it becomes a very promising technology for controlling plant viruses. An alternative approach to the utilization of transgene-derived miRNAs is the direct application of PTGS inducer, long double-strand RNA (dsRNA) (Tenllando et al., 2003). Spray onto the surface of plant leaves with bacteria-produced double-strand RNA was shown to be efficient against different viruses (Tenllando et al., 2003). Limitations of this approach may rely on the maintenance of effectiveness of dsRNA, costs of dsRNA production and safety of large-scale application of dsRNA in the field. Advances in the understanding of molecular virus-plant interactions as well as the virus life cycle will certainly assist the development of novel antiviral strategies. One emerging technology is to induce recessive resistance. The rational of this approach is that plant viruses have a relatively small genome that encodes a limited number of proteins and thus must depend on host gene products to fulfill their life cycle. Silencing or mutation of host

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factors required for virus infection will generate genetic resistance to the virus. Over the last decade, a number of host factors required for potyvirus infection have been identified and examples include eIF4E, eIF(iso)4E, eIF4G, PABP, AtRH8 and PpDDX (Lellis et al., 2002; Ruffel et al., 2002, 2005; Nicaise et al., 2003, 2007; Gao et al., 2004; Kang et al., 2005; Decroocq et al., 2006; Bruun-Rasmussen et al., 2007; Dufresne et al., 2008; Huang et al., 2010). Recently Piron et al. (2010) have reported a successful story about how to deploy host factors for the development of resistance to potyviruses in tomato. They exploited a chemical-induced tomato mutant population, screened for mutants of eIF4E and eIF4G using a reverse genetic tool, TILLING (Targeting Induced Local Lesions IN Genomes), and identified a splicing mutant of eIF4E. The mutant was shown to be immune to two plant potyviruses (Piron et al., 2010). This example is particularly encouraging as the mutant did not involve in genetic transformation. The technology may be adapted for the generation of resistance to viral diseases in virtually any crops including soybean.

5. Conclusion remarks Soybean is one of a few most important crops in the world and serves as a principal dietary food and oil source. Soybean oil is also considered a promising alternative to fossil oil. SMV is one of the major biotic factors that adversely affect soybean production. In this chapter, we have briefly discussed SMV as a pathogen, SMV-soybean interactions and its current and future control strategies. The current measures to control damages caused by SMV are (1) the development and use of soybean cultivars carrying at least one resistance gene, (2) the use of SMV-free seeds, (3) the selection of proper planting time, and (4) the control of aphid with pesticides. Along with the climate change (such as global warming), the emergence of new severe isolates (including resistance-breaking isolates) and new vectors such as Aphis glycines, and an increasing rate of synergic infections of SMV and other soybean viruses, the current measures are becoming less and less effective. After extensive screening over a considerable time, isolation of new natural SMV resistance genes from existing germplasm is not optimistic. New technologies must be developed to deal with SMV. One of future research directions might be on identification of soybean genes that are required for SMV infection. These genes will be new targets for manipulation against SMV. SMV starts from infected seeds but spreads from plant to plant by aphids. Unfortunately, very little study has been done on SMV-aphid interactions. This may be a topic of collaborative research between plant virologists and entomologists. Recently, the complete draft sequence of soybean has been released (Schmutz et al., 2010). The availability of the complete soybean genome sequence will certainly facilitate the molecular cloning and characterization of the three R genes and elucidating their resistance signaling pathways, and provide a better understanding of the co-evolutionary events of the R genes and the SMV genome. Information from these studies will help develop novel strategies against SMV and other related viruses.

6. Acknowledgements The work carried out in the Wang laboratory was supported in part by Ontario Soybean Growers, the Natural Sciences and Engineering Research Council of Canada, and Agriculture and Agri-Food Canada. The authors are very grateful to Dr. R.V. ChowdaReddy for critical reading of the manuscript.

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Wang, A. (2009). Soybean mosaic virus: research progress and future perspectives. Proceedings of World Soybean Research Conference VIII (www.wsrc2009.cn), Beijing, China. Wang, A., Tian, L., Huang, T.-S., Brown, D.C.W., Svircev, A.M., Stobbs, L.W., Miki, B.L.A. & Sanfaçon, H. (2009). The development of genetic resistance to Plum pox virus in transgenic Nicotiana benthamiana and Prunus domestica. Acta Horticulturae 839, 665672. Wang, X., Eggenberger, A.L., Nutter Jr. & Hill, J.H. (2001). Pathogen-derived transgenic resistance to soybean mosaic virus in soybean. Molecular Breeding 8, 119-127. Wang, X., Gai, J. & Pu, Z. (2003). Classification and distribution of strains of Soybean mosaic virus in middle and lower Huanghuai and Changjiang river valleys. Soybean Science 22, 102-107. (in Chinese). Wang, Y., Zhi, H. & Guo, D. (2005). Classification and distribution of strain groups of soybean mosaic virus in northern China spring planting soybean region. Soybean Science 24, 263-268. (in Chinese). Waterhouse, P.M., Wang, M.B. & Finnegan, E.J. (2001). Role of short RNAs in gene silencing. Trends in Plant Science 6, 297-301. Wei, T., Huang, T.-S., McNeil, J.R., Laliberte, J-F., Hong, J., Nelson, R.S. & Wang, A. (2010a). Sequential recruitment of the endoplasmic reticulum and chloroplasts for plant potyvirus replication. Journal of Virology 84, 799-809. Wei, T., Zhang, C., Hong, J., Xiong, R., Kasschau, K.D., Zhou, X., Carrington, J.C. & Wang, A. (2010b). Formation of complexes at plasmodesmata for potyvirus intercellular movement is mediated by the viral protein P3N-PIPO. PLoS Pathogens 6(6), e1000962. Wei, T. & Wang, A. (2008). Biogenesis of cytoplasmic membranous vesicles for plant potyvirus replication occurs at the endoplasmic reticulum exit sites in a COPI- and COPII-dependent manner. Journal of Virology 82, 12252-12264. Wen, R.H. & Hajimorad, M.R. (2010). Mutational analysis of the putative pipo of Soybean mosaic virus suggests disruption of PIPO protein impedes movement. Virology 400, 1-7. Wetzel, T., Ebel, R., Moury, B., Le Gall, O., Endisch, S., Reustle, G. M. & Krczal, G. (2006). Sequence analysis of grapevine isolates of Raspberry ringspot nepovirus. Archives of Virology 151, 599-606. Whitham, S., Yamamoto, M. & Carrington, J.C. (1999). Selectable viruses and altered susceptibility mutants of Arabidopsis thaliana. Proc Natl Acad Sci USA 96, 772-777. Whitham, S., Yang, C. & Goodin, M.M. (2006). Global impact: elucidating plant responses to viral infection. Molecular Plant-Microbe Interactions 19, 1207-1215. Wileman, T. (2006). Aggresomes and authophagy generate sites for virus replication. Science 312, 75-878. Yu, Y., Maroof, M., Buss, G., Maughan, P. & Tolin, S. (1994). RFLP and microsatellite mapping of a gene for Soybean mosaic virus resistance. Phytopathology 84, 60-64. Zhan, Y., Zhi, H., Yu, D. & Gai, J. (2006). Identification and distribution of SMV strains in Huang-Huai valleys. Scientia Agricultura Sinica (in Chinese) 39, 2009-2015. Zhang, C., Hajimorad, M.R., Eggenberger, A.L., Tsang, S., Whitham, S.A. & Hill, J.H. (2009). Cytoplasmic inclusion cistron of Soybean mosaic virus serves as a virulence

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16 Evolution of Soybean Aphid Biotypes: Understanding and Managing Virulence to Host-Plant Resistance Andrew P. Michel1, Omprakash Mittapalli1 and M. A. Rouf Mian2,3 1Department

of Entomology, The Ohio Agricultural Research and Development Center, The Ohio State University; 2Department of Horticulture and Crop Sciences, The Ohio Agricultural Research and Development Center, The Ohio State University; 3USDA-ARS: Corn and Soybean Research Unit, Wooster, OH U.S.A. 1. Introduction The soybean aphid (Aphis glycines Matsumura) has rapidly become one of the most significant insect pests of soybean (Glycine max) worldwide (Ragsdale et al., 2007). The rise of soybean aphid in importance is in large part due to the invasion of North America ca. 2000, presumably coming from its native range in Asia (Ragsdale et al., 2004). In the first few years of the North American invasion, the soybean aphid spread across much of the NorthCentral US and the provinces of Ontario and Québec. Its current distribution includes over 80% of the soybean growing region of the US and Canada (Vennette & Ragsdale, 2004). Worldwide, the distribution includes much of East Asia (China, Japan, The Philippines, South Korea, Indonesia, Malaysia, Thailand, Vietnam, and Russia), all of which likely represents the ancestral range (Footit et al., 2006). Options for management and control of soybean aphid are limited. As an alternative to chemical insecticides, host-plant resistance is a common method of aphid control (Van Emden, 2007), which uses plant hosts with genetically inherited traits that enable the plant to withstand pest attack better than a plant lacking these traits (Smith, 2005). Although soybean aphid resistant varieties have been studied in China (Wu et al., 2004), new soybean varieties have been developed with resistance to the soybean aphid specifically for use in North America (Hill et al., 2004, 2006a,b; Mian et al., 2008; Zhang et al., 2009; Hill et al., 2010). Varieties for North American commercial use were available for the first time in 2010. However, the host-plant resistance strategy is complicated because of rapid evolution of soybean aphid populations (i.e. biotypes) that have overcome host-plant resistance (i.e. virulence). Biotypes can be defined as “populations within an arthropod species that differ in their ability to utilize a particular trait in a particular plant genotype” (Smith, 2005). The presence of soybean aphid biotypes with virulence to host-plant resistance varieties before these varieties were commercially released suggests that the soybean aphid can rapidly adapt to these new lines and thereby threaten the effectiveness and sustainability of the host-plant resistance strategy.

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Fig. 1. Soybean aphid infestation on soybean To fully understand the threat to host-plant resistance, research must focus not only on developing new varieties of soybean-aphid resistant soybean, but also on the genetic, genomic and evolutionary factors involved in adaptation and spread of soybean aphid biotypes. This chapter will review the current status of soybean aphid biotypes, highlight functional and population genetic and genomic studies that may provide clues to uncovering the basis for soybean aphid biotype adaptation, and outline future studies that will be necessary if resistant soybean varieties are to be used and preserved as a soybean aphid management strategy.

2. Soybean aphid biology The soybean aphid is among the more than 450 named species within the genus Aphis (Blackman & Eastop 2007; Couer d’acier et al., 2007). However, despite the total number of named species, the evolution of the genus is difficult to interpret and remains largely unresolved, mostly due to a recent adaptive radiation (Couer d’acier et al. 2007; Kim & Lee 2007). This is especially true for the 3-4 species groups: A. fabae, A. craccivora, A. gossypii and A. frangulae, with the latter 2 groups sometimes lumped together as the A. gossypii group (Kim & Lee, 2008). Kim & Lee (2008) placed A. glycines relatively basal within the A. gossypii group and to A. glycines itself, based on mitochondrial and nuclear DNA. However, Wu et al. (2004) report that A. glycines and A. gossypii can produce viable offspring in the laboratory and in nature, as well as share an overwintering host, Rhamnus davurica Pal. The apparent disconnect between genetic and mating evidence points to the difficulty of establishing accurate species relationships within Aphis. The original distribution of A. glycines is described as Oriental and East Asian (Footit et al. 2006), which overlaps with the original cultivation of soybean (Qiu & Chang 2010). Its initial spread was probably related to the expansion of soybean. There are at least two known recent introductions since 2000. The soybean aphid was reported to be found in Australia (Fletcher and Desborough, 2000), although any concern of achieving invasive or pest status appears to

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be unfulfilled. The more serious North American invasion was also first detected in 2000 in Wisconsin (Ragsdale et al., 2004; 2007), within 250 km of the city of Chicago, a major international trading center by both sea and air travel. How the soybean aphid invaded, the size of the invasion, and where it came from is still unknown. Nonetheless, within 3 years of its arrival, the soybean aphid had spread to 21 US states and 3 Canadian provinces, placing an estimated 80% of the North American soybean crop at risk (Vennette and Ragsdale, 2004). The current distribution is widespread across North America, although the heaviest infestations and economic damage occurs within the Midwestern US, and the Great Lakes states and the provinces of Ontario and Québec, Canada. The life cycle of the soybean aphid in North America is similar to what is known from its native range (Wang et al. 1994). It is a typical heteroecious, holocyclic species, alternating among sexual reproduction on primary hosts and asexual reproduction on secondary hosts (Ragsdale et al. 2004). The primary hosts are various buckthorn trees (Rhamnus spp., Voegtlin et al. 2005; Yoo et al. 2005). Buckthorn is a widely distributed herbaceous shrub in the flowering plant family Rosales. This shrub is commonly used for horticultural and landscaping, especially along hedges and fence rows. These characteristics led to the introduction of buckthorn into North America from Europe by the early 19th century. It quickly became invasive and led to an invasion meltdown involving 10 other Eurasian plant, animal and pathogen species (Hiempel et al. 2010). In North America, overwintering of the soybean aphid mainly occurs on Rhamnus cathartica (common buckthorn, Figure 3), but can also occur on R. alnifolia (alderleaf buckthorn), R. lanceolata (lanceleaf buckthorn) and F. alnus (glossy buckthorn) (Voegtlin et al. 2005; Yoo et al. 2005; Hill et al. 2010). Across its native Asian range, R. davurica and R. japonica are the main hosts (Yoo et al. 2005).

Fig. 2. Soybean aphids on buckthorn (R. cathartica). Note the presence of winged forms (most likley males (androparae) and females (gynoparae) and wingless females (oviparae). Picture taken on October 6, 2006 in Toledo, OH, USA. Photo Credit: R. B. Hammond, The Ohio State University.

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The secondary host is largely restricted to soybean (Blackman & Eastop 2000; Ragsdale et al. 2004) although some additional hosts may play a minor role (Blackman & Eastop 2000). On soybean, populations can double every 6-7 days (Ragsdale et al. 2007), with close to 15 generations occurring within one growing season. Fully developed adults can produce more than 9 nymphs per day and total more than 60 nymphs in a lifetime under laboratory conditions (McCornack et al. 2004). During autumn, soybean aphids return to primary host, probably cued by soybean senescence and the typical environmental changes associated with the change of seasons. Feeding of the soybean aphid on soybean affects various plant characteristics including seed quality and size, pod number, and plant height, and, most importantly, overall yield (Hill et al., 2004; Ragsdale et al. 2007). Dramatic yield losses of up to 50% have been reported under heavy soybean aphid infestations (Wang et al., 1994), and evidence suggests even small infestations can lead to disruptions in normal soybean physiology (Macedo et al. 2003). The soybean aphid also poses a potentially large threat to soybeans because of its ability to vector a variety of viruses such as Potato Y virus, Alfalfa mosaic virus, Soybean mosaic virus, Cucumber mosaic virus and possibly Soybean dwarf virus to soybean and other crops (Iwaki et al. 1980; Hill et al. 2001; Clark & Perry 2002), adding to the economic importance of this pest. Virus transmission is especially important in food grade, seed grade and organic soybean production, since known thresholds are only based to prevent yield loss. Economic losses from the soybean aphid have been calculated between US$2.4 and US$4.9 billion and annually (Song et al. 2006; Kim et al. 2008).

3. Soybean aphid management There are few methods for controlling soybean aphid. Chemical control of soybean aphid is effective but requires frequent scouting by trained individuals and use of established thresholds (see Ragsdale et al. 2007). Although effective, control with insecticides may be environmentally hazardous and harm beneficial and natural enemies, leading to repeated applications and the increased risk of environmental degradation (Costamagna and Landis 2006). A safer and better strategy to combat the soybean aphid may be obtained by hostplant resistance. A few historical studies have measured soybean aphid resistance in the soybean aphid (Wu et al. 2004). The North American invasion has necessitated a resurgence in screening soybean lines for resistance to the soybean aphid. In various studies, host-plant resistance in the form of both antibiosis (reduced survival/fecundity) and antixenosis (nonattractive/repellency of establishment) has been found. To date, 4 independent Rag (Resistance to Aphis glycines) genes have been described: Rag1 (Hill et al. 2004), Rag2 (Mian et al. 2008), Rag3, and Rag4 (Zhang et al. 2009). RagI was first identified in the soybean cultivar Dowling and maps to soybean linkage group (LG) M (Hill et al. 2006a; Li et al. 2007). Similar resistance was found in the cv. Jackson, as was found to map at the same location as Dowling, suggesting a possible allelic difference (Hill et al. 2006b). Rag2 was discovered in two plant introductions (PIs): 243540 and 200538 and maps to LG F (Kang et al. 2008, Mian et al. 2008, Hill et al. 2009). Zhang et al. (2009) found 2 resistance genes in PI 567541B, Rag3 and Rag4. Rag3 maps close to Rag1 and Rag4 maps on LG F, but is not near Rag2. There are a few other promising PIs (e.g. 567301B, Mian et al. 2008) that may yet reveal additional major or minor effect Rag genes. The first North American commercially released soybean aphid resistant cultivar (starting in 2010) will contain Rag1.

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The host-plant resistance strategy is complicated by soybean aphid biotypes. The resistant soybean lines with the Rag1 genes provided strong resistance against soybean aphids collected from Illinois (Hill et al. 2004, 2006a, b). However, when tested against soybean aphids collected in Ohio, these soybean lines were ineffective, and Ohio aphids were able to survive and reproduce on Rag1 as effectively as on susceptible plants (Kim et al. 2008). Mian et al. (2008a) noted that the same aphids that had survived on Rag1 did not survive on Rag2 (PI 243540). These biotypes, defined by their differential survivability on Rag genes, have been named biotype-1 and biotype-2 where biotype-1 is defined as avirulent to Rag1, and biotype-2 is defined as virulent to Rag1. Hill et al. documented the presence of biotype-3. This biotype was initially collected in 2007 overwintering on Frangula alnus in Indiana. Testing of biotype-3 on both Rag1 and Rag2 varieties revealed that while Rag1 resistance was retained, there was no significant difference no significant difference in colonization, survivability and reproduction with biotype-3 on Rag2 when compared to susceptible varieties (Hill et al. 2010). Thus, biotype-3 is defined as avirulent to Rag1 but virulent to Rag2. Table 1 represents the current status and definitions of known biotypes—these initial data suggests there may be more biotypes present that have yet to be identified. Resistance Gene or soybean PI Rag1 Rag2 PI 567301Ba PI 567541B PI 437696

Biotype 1 avirulent avirulent avirulent avirulent avirulent

Biotype 2 virulent avirulent avirulent avirulent avirulent

Biotype 3 avirulent virulent NDb moderately virulent avirulent

From Mian et al. 2008 Not determined

a b

Table 1. Known soybean aphid biotypes and virulence relationships (Hill et al. 2010) Given the presence of soybean aphid biotypes, questions have arisen on the sustainability and efficacy of incorporating host-plant resistance for soybean aphid management. The evolution of soybean aphid biotypes, and therefore the usefulness of host-plant resistance, depends on 1) the genetic variation for virulence, and 2) how this variation can spread locally, regionally and globally. Unfortunately, few molecular resources exist for the soybean aphid (but see Bai et al. 2010) which limits researchers’ ability to infer the durability of soybean aphid resistant soybean. Further studies on aphid biotype evolution are therefore necessary if host-plant resistance is to be a viable aphid management strategy. The following sections will focus on some preliminary studies that may provide the foundation for future work in understanding biotype evolution.

4. Genetic basis for biotype evolution Many species within the family Aphididae have biotypes in association with host-plant resistance (Smith 2005)—van Emden (2007) lists 14 species not including the soybean aphid. Three characteristics of the aphid life cycle make this group more amenable to biotype adaptation than other insect pest species. First, most aphids carry bacterial endosymbionts (e.g. Buchnera, Hamiltonella, Rickettsia, Arsenophonus, Regiella, Serratia) which provide essential amino acids and may be involved in biotype formation and aphid defense (Ruggle

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and Gutierrez 1995; Birkle and Douglas 1999; Moran and Wernegreen 2000; Wille and Hartman 2009; Oliver et al. 2010). These endosymbionts have been associated with different biotypes or host-races of insects, presumably because of the different nutrients and amino acids afforded by different hosts (Simon et al. 2003; Chiel et al. 2007). For example, Ruggle and Gutierrez (1995) indicated virulence to Lucerne (alfalfa) varieties is symbiont based. Second, aphids feed exclusively on plant phloem. Typically, this type of feeding induces consistent responses within plants through interactions with aphid saliva (Mutti et al. 2008), and places great importance on the role of the salivary glands in biotype adaptation. Previous research suggests specific factors found in aphid saliva are important in biotype adaptation. For example, greater pectin methylase activity in saliva of Schizaphis graminum biotypes leads to resistance breakdown in sorghum (Dreyer and Campbell 1984), and saliva related proteins may be involved in Diuraphis noxia biotype adaptation against wheat (Lapitan et al 2007). Third, the rapidity with which aphids are able to adapt is no doubt aided by their complex life cycle. Most species are holocyclic (alternating between primary and secondary hosts) and heteroecious (undergoing sexual and asexual reproduction), although variations and phenotypic plasticity are common (Moran 1992; Blackman and Eastop 2000; 2007). Any adaptation that evolves during the asexual stage can quickly become common and spread, as the generation time can be as little as a few days. However, despite the frequency at which biotypes evolve in aphids, very little is known about the genetic mechanisms of biotype evolution in general and in the soybean aphid in particular. Only in few studies (Dreyer and Campbell 1984; Lapitan et al. 2007) have mechanisms been explained, but yet the gene(s) involved still remain elusive. This lack of knowledge also extends to the genetic and physiological mechanisms that provide soybean aphid resistance to the plant. Although genetic and quantitative trait loci (QTL) mapping has located genomic regions responsible for resistance, the actual gene(s) conferring resistance at each locus has not been identified or cloned. Using cDNA microarrays and various stages of soybean aphid infestation, 140 genes showed differential expression in among resistant (cv. Dowling—Rag1) and susceptible varieties (Li et al. 2008). These genes were related to many molecular processes including cell wall, defense, metabolism, and signaling. Verification of 3 defense related genes using quantitative PCR showed a more rapid and prolonged induction in cv. Dowling versus susceptible and illustrated a possible earlier inducement of genes related to resistance. Interestingly, some of the genes shared a common mechanism with the pathogen Pseudomonas syringae. In a recent study, Chiozza et al. (2010) compared amino acid profiles between soybean lines LD05-16060 (resistant, Rag1) and its susceptible isoline, SD01-76R. Various amino acids show both constitutive and induced concentration differences. Most amino acids showed a higher constitutive concentration in the susceptible line at the V6 stage, including αaminobutyric acid, aspargine, glutamine, glutamic acid, histidine, proline and serine. However, – α-aminobutyric acid, glutamine, histidine, and tryptophan all had significantly higher quantity in resistant plants during R4 and R5 stage than susceptible plants. Chiozza et al. (2010) suggest that the decrease of aspargine and glutamic acid could have a nutruitive effect on the soybean aphid related to resistance. Indeed, performance of the soybean is lower when glutamic acid is not available (Wille & Hartman 2009). In addition, most aphids carry endosymbiotic bacteria (Buchnera aphidocola) which are involved in transport and synthesis of essential amino acids, including glutamic acid (Liadouze et al. 1995; other sources). If indeed a decrease in glutamic acid provides resistance, it is tempting to speculate that soybean aphids virulent to Rag1 may harbor a different B. aphidicola strain that is

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efficient in metabolic pathways involving glutamic acid, thereby compensating for the low quantities available in Rag1. The resistance offered in other soybean aphid resistance varieties has not been investigated as much as Rag1. Recent studies have now laid the groundwork for detailed investigations into the genetics and mechanisms of soybean aphid resistance and aphid biotype adaptation. First the whole genome sequence for soybean has been published (Schmutz et al. 2010), which will allow for better annotation of soybean aphid resistance QTLs and well as provide higher-resolution markers for fine-scale mapping. In addition, Bai et al. (2010) presented the first significant sequencing resource for the soybean aphid. Over 278 million base pairs were sequenced, resulting in 56,688 genomic sequences and 19,293 transcripts (i.e. expressed sequences). In addition, 39,822 bp of sequence shared similarity with the obligatory endosymiont Buchnera aphidocola and to the facultative endosymbiont Hamiltonella defensa.

5. Population biology issues: The distribution and spread of biotypes The ability and potential of virulence to spread across the soybean growing region is critical to the increase in virulent biotype frequencies and the decrease in host-plant resistance durability. The insect resistance management (IRM) strategy for transgenic corn is a decent analogy to a possible solution for preserving susceptible soybean aphids. In the IRM system, a maximum percentage of insect-resistant corn can be planted (usually 80-95%) with the remainder reserved for planting a susceptible variety (refuge). The refuge provides a proportion of insects that do not encounter the intense selection pressure of resistant crops, and hence should maintain susceptible alleles in the insect population. These refuge insects are also available to mate with any potential resistant insects, thereby producing susceptible offspring (assuming resistance in insects is recessive). However, the soybean aphid is not a typical insect pest as explained above. The evolution of a specific biotype does not necessarily indicate the durability of a particular resistant variety will be impacted. There are various factors that can affect how quickly a biotype can increase in local frequency, spread regionally and globally, and transfer genetic variation for virulence into future generations: fitness depends both on survivability and mating success. Its obligatory asexual and sexual phase, as well as its reliance on a single plant for mating and overwintering, complicates the potential spread of virulence throughout populations. During the soybean aphid life cycle, there are three specific migration events that can all affect how virulence can spread. First is the summer, asexual generation migration among soybean fields, second is the autumn return from soybean to buckthorn, and third is the spring migration from buckthorn to soybean. During these migration events, soybean aphids are at the mercy of population genetic, demographic, landscape, ecological, environmental and evolutionary forces that can either promote or abate the spread of biotypes. Summer Migration. Once soybean aphids colonize soybean, asexual reproduction can rapidly increase population sizes. Like many aphid species (Dixon 1988), temperature plays a large role in the reproductive rate for the soybean aphid. In laboratory conditions, development thresholds range from 9.5ºC to 34.9ºC, with 27.8ºC found to be the optimal temperature (Hirano et al. 1996; McCornack et al. 2004), with population doubling times ranging from 1.5 days (at 25ºC) to 1.9 days (at 20ºC and 30ºC) (McCornack et al. 2004). In the field, Ragsdale et al. (2007) found an average population doubling time of 6.8 days. Thus, just by temperature alone, the potential of a virulent biotype to rapidly increase in frequency is high. However, other important factors may play a role such as plant host quality and presence of natural enemies (Ragsdale et al. 2007; Costamagna a& Landis 2006).

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Fig. 3. Aphid reproduction on a detached cv. Jackson leaf. Each leaf started with 1 adult aphid, figure shows reproduction after 7 days. A) Biotype 2; B) Biotype 1. Solid arrows point to adults, broken arrows point to nymphs. After the initial colonization of soybean, there is a slight delay before alates are produced that can migrate and infest other surrounding fields. During this phase, there are likely no restrictions to migration, and alates can spread across fairly large distances. Using microsatellite molecular markers, Michel et al. (2009), showed insignificant amounts of genetic differentiation over 1,500 km. This pattern was in contrast to populations collected earlier, i.e. before the late summer migration, where populations were more genetically distinct. However, this study could not rule out that the distribution of genetic variation was due to a more a step-wise manner of migration (geographic scales necessary for accurate isolation by distance assessment were not included). Nonetheless, there appears little to impede the spread of a virulent biotype among soybean fields. The rapid invasion during which the soybean aphid spread to over 20 US states and 3 Canadian provinces in 3 years also testaments to the large migratory ability. If indeed there are few limits on migration, then estimating the likelihood of biotype spread depends on the initial frequency and distribution, and how quickly these frequencies can increase. However, research is limited in both areas. Recently, Michel et al. (2010) developed biotype determination assay using detached resistant soybean leaves (Figure 3). In cv. Jackson (Rag1-like, see above) and PI243540 (Rag2), similar levels of aphid resistance was found between detached leaves and whole plants, suggesting that resistance is retained.

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This was further corroborated by comparing growth rates (i.e. net fecundity or the number of nymphs) of biotype 1 and biotype 2 on detached cv. Jackson, where the growth rate of biotype 2 was consistently more than double that of biotype 1 after the second day. The growth rate estimates for biotype 1 and biotype 2 can then be used as baselines in comparisons to unknown field samples. In 2009, aphids were collected in total of 8 fields (6 in Ohio, and 1 each in South Dakota Kansas) and reared on detached cv. Jackson leaves. Table 2 shows the frequency distribution of aphids with net fecundity matching biotype 1, between biotypes (moderately virulent to Rag1), biotype 2, or exceeding biotype 2 (A.P. Michel, unpublished). First, the results show much variability, even among fields from the same state. The frequency of aphids that had similar growth rates as biotype 2 or greater ranged from 0 to 40%. This variation also persisted regionally—Ohio had a relatively higher rate of Rag1 virulent aphids, Kansas had a more moderate level (14%), and South Dakota had no aphids which matched or exceeded growth rates of biotype 2. Aphid colonies have been found on Rag1 bearing plants in South Dakota (K. Tilmon, South Dakota State University, personal communication), but whether this represents 23% of aphids moderately virulent to Rag1, or actual biotype 2 aphids undetected with the above data is unknown. In any case, the data illustrates that, at least for Rag1, virulence is widely distributed. It is also important to note, however, that these biotype 2 frequencies reflect natural genetic variation without the presence of a large selection pressure. Number of aphids that produced: 20 nymphs

Average

OH-1

25

14 (56%)

11(44%)

0

0

8.1

OH-2

40

12 (30%)

26 (65%)

1 (3%)

1 (3%)

9.1

OH-3

26

12 (46%)

10 (38%)

3 (12%)

1 (4%)

9.3

OH-4

40

10 (25%)

16 (40%)

7 (18%)

7 (18%)

12.1

OH-5

36

9 (25%)

13 (36%)

8 (22%)

6 (17%)

12.1

OH-6

35

6 (17%)

15 (43%)

7 (20%)

7 (20%)

13.9

SD

30

23 (77%)

7 (23%)

0

0

4.4

KS

21

9 (43%)

9 (43%)

3 (14%)

0

8.7

11.9 (40%)

13.4 (40%)

3.6 (10%)

2.8 (10%)

Overall e

Number of aphids tested 7.12 is the upper 95% Confidence Interval (CI) for biotype 1 c15.54 is the lower 95% CI for biotype 2 d18.50 is the upper 95% CI for biotype 2 eamong all locations a

b

Table 2. Frequency and geographic distribution of soybean aphid virulence to cv. Jackson Given the migration ability and biotype frequencies, researchers must next address two critical questions. First, is the comparative level of fitness between biotypes in different resistant plant

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environments and the resulting different selection pressures. Initially, host-plant resistant lines may be of limited release and use. Susceptible hosts could then act as a sink for virulent biotypes, but only if they are less fit than avirulent biotypes. If virulent biotypes outcompete avirulent biotypes even in susceptible environments, then virulence will spread much more rapidly. Second, the increase and spread of virulent biotypes may also depend on the initial starting frequency. For example, how much more quickly will biotype 2 increase in frequency with a initial frequency of 2% when compared to 20%? More importantly, as soybean producers look to an integrated management approach with insecticide seed treatments, soybean aphid resistant genes, and chemical applications, the rate at which (or if) these frequencies reach economic threshold (Ragsdale et al. 2007) must be estimated. These estimations are crucial as the frequency of biotypes produced in soybean fields represent the overall potential population that is available for mating and transmitting genetic variation for virulence to the next generation, assuming soybean aphids can successfully colonize their primary host. Finally, and most importantly, is the level of selection pressure placed on soybean aphid populations. Increased adoption of a certain resistant soybean variety will rapidly increase virulent biotypes by increasing the likelihood of successful migration. For example, assuming migration is random among soybean fields, biotype 2 is likely to survive if adoption of Rag1 varieties is high (i.e. high selection pressure). But if Rag2 varieties are also widespread throughout the landscape, then a certain number of biotype 2 aphids will suffer mortality and virulence for Rag1 will be slower to increase in frequency (until, of course, a biotype arises with virulence for both Rag1 and Rag2). Autumn Migration. As stated earlier, the primary or overwintering host of the soybean aphid is buckthorn. However, there is a drastic difference in distribution between the soybean aphid, especially during the peak summer months, and the presence of buckthorn. The soybean aphid has been found as far south as Alabama and Georgia in the USA (about 34ºN latitude), but buckthorn is mainly found in the North Central US and Great Lakes region of the US and Canada and rarely present or abundant at latitudes below 41ºN (Ragsdale et al. 2004). Thus, in some cases, soybean aphids present in southern US states would have to migrate long distances in order to find its primary host plant. It is untested whether or not these more southern aphids from soybean can successfully return to buckthorn, but previous population genetic studies suggest no biological restraint on long-distance migration (Michel et al. 2009). The relative contribution of these aphids to the mating population may be more important, i.e. if long-distance buckthorn colonization occurs but at lower success rates than for aphids where buckthorn is in closer proximity. Understanding possible differences in migration success from geographical populations is critical in estimating the durability of host-plant resistance. For example, if it is shown that soybean aphids from southern US states are less likely to colonize buckthorn, then selection pressures may be inconsequential in these areas because any new virulent biotype is less likely to transmit its genetic variation to the next generation. If biotypes cannot mate then this ultimately leads to an evolutionary dead-end. Alternatively, where success rates of buckthorn colonization are high, the deployment of host-plant resistant varieties, as well as biotype frequencies, must be more heavily managed, as genetic variation for virulence is more likely to persist. There are other environmental and ecological factors to consider in addition to successful colonization. Once buckthorn is colonized, soybean aphids must survive long enough for oviposition to occur. Natural enemies can follow aphid migration on to buckthorn (Gray 2006) but to date no study has examined the level of biological control on buckthorn. McCornack et al. (2005) noted that alates may perish at 15-18ºC above the supercooling

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point (calculated at -14.9ºC and-15.2ºC for oviparae and gynoparae, respectively). In an extremely cool autumn, significant mortality can occur before oviposition. One of the largest, if not the largest, migratory flight from soybean to buckthorn occurred in 2009 across North America (Gray 2009a). The large populations on buckthorn, combined with the cooler, wet conditions led to a fungal pathogen outbreak (Gray 2009, Issue No. 24, Article 5/November 6, 2009). Although the particular fungal pathogen causing mortality has not been identified, numerous fungal pathogens have been found from soybean aphid (Nielsen and Hajek 2005). Massive mortality ensued, and oviposition was minimal. Thus, even though migration and colonization was successful across a wide geographical area, few soybean aphids emerged the following spring (Hammond et al. 2010). Spring Migration. Soybean aphid eggs are laid in late autumn near buds or on the bark. Egg hatch is most likely determined by temperature and largely coincides with bud swell of buckthorn (Bahlai et al. 2007). Bahlai et al. (2007) were able to develop a predictive model for egg hatch based on temperature alone, but the inclusion of heat units from solar radiation dramatically improved the accuracy of the model, suggesting a microclimate effect (see also Welsman et al. 2007). Fundatrices, or stem mothers, are the form of aphids that hatch from eggs. This behavioral form is usually plump and large, and begins asexual reproduction. At least 2-3 assexual generations occur on buckthorn, and growth rates can be dependent on spring temperatures, up to 1.5 generations per day at 24ºC (Bahlai et al. 2007). Various ecological and environmental factors can significantly influence the spring population size and the number of alates that can eventually colonize soybean. McCornack et al. (2005) showed that successful egg overwintering is not likely to occur below -34ºC, which is the supercooling point of soybean aphid eggs (the supercooling point is the temperature at which freezing of tissue occurs). In addition, reproduction on buckthorn can be limited by temperature. In 2007, an extremely warm spring encouraged buckthorn bud break and soybean aphid egg hatch. Within the next few weeks, temperatures rapidly decreased and remained below freezing, which likely caused fundatrix and spring viviparae mortality (Hammond & Eisley 2007). Of the aphids that remain on buckthorn, alates are eventually formed, perhaps by a density dependant mechanism (Bahlai et al. 2007), and migrate to soybean fields. The colonization of soybean from buckthorn remains complicated in both the timing and the influence of landscape, particularly the proximity to buckthorn (Ragsdale et al. 2004; Bahlai et al. 2010). Ragsdale et al. (2004) noted that colonization occurred in only 4 of 42 locations of soybean aphid infested buckthorn stands that contained potted soybean plants. This is in keeping with the observation that, in some locations, no local sources of soybean aphid on buckthorn can be found as soybean begins to emerge. In addition, observations from Minnesota, Illinois and Indiana showed that on occasion alates have been formed on buckthorn several weeks before soybean emergence, though there is no other known alternate host to support these early migrants. This “phenological disjunction” between the primary and secondary hosts of soybean and the aphid itself can lead to paucity of aphid genetic founder material for soybean colonization and influence biotype frequencies. If a genetic bottleneck during this migration event occurs, then early populations of soybean aphids should contain less genetic diversity and fewer clones than populations collected later in the season. Using microsatellite molecular markers, Michel et al. (2009) compared clonal and genetic diversities among early collections from Michigan (July 7, 2008) and Ontario (July 22, 2008) to eight other U.S. populations collected after August 15, 2008. Populations from Michigan and Ontario had a significantly lower number clones per

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population than mid-August collections (average genotypic diversity, GD: 0.47 and 0.82, for early and late, respectively, Mann-Whitney U = 16.0, P = 0.02), and significantly less clones (12.5 and 28.9, respectively, Mann-Whitney U = 16.0, P = 0.02), even when taking into account differences in population sample size. Both measures were significantly correlated with time (GD: R = 0.53, P = 0.013; clones: R2 = 0.51. P = 0.02). Later season populations contained higher levels of clones and GD, presumably due to the immigration of other clones from nearby fields. Thus, molecular evidence supports a genetic bottleneck during soybean colonization. In this case, initial population sizes of soybean aphids in soybean are low and consist of very few clones. Diversity is then a result of the interplay between genetic drift (including random mortality from environmental conditions or natural enemies), and selection through clonal amplification. Clonal amplification refers to the rapid increase in frequency in certain asexual clones through selection which is quite common in many aphid species during the asexual phase (Vorburger et al. 2003; Vialette et al. 2005), and likely occurs in soybean aphid (Michel et al. 2010). Currently, the use of soybean aphid resistant varieties is limited, therefore selective pressure and clonal amplification of specific virulent biotypes is not likely to occur. If adoption increases, however, the spread of a virulent biotype might depend more on its ability to colonize a particular resistant soybean variety because, if successful, clonal amplification can quickly increase its frequency. Random mortality during the spring colonization of soybean suggests that colonization of soybean may be restricted or most important at small spatial scales, dependant on buckthorn abundance and proximity. Using population dynamic modeling and the Akaike’s information criterion (which compares the likelihood of competing models), Bahlai et al. (2010) showed that, indeed, the presence or absence of buckthorn was highly related to soybean aphid colonization, and the estimated number of buckthorn shrubs best predicted aphid density, although the distances explored were less than 5km. As soybean aphids likely use both short and long distance dispersal (Zhang et al. 2008; Michel et al. 2009) expanding these models across larger distances would improve the ability of identifying buckthorn source populations for soybean colonization. These migration and colonization events may have a profound effect on the level and distribution of biotypes, and ultimately how virulence evolves on a regional scale. If, for example, local soybean aphid populations can adapt to local fields of resistant varieties, then we might expect the sustainability of these varieties to be low, as each local population represents an opportunity for adaptation. Alternatively, if adaptation to resistant varieties depends on distinct virulent genotypes, then virulence evolution will be at the mercy of migration and colonization patterns and the stochastic demographic events associated with such movement. If substantial mortality occurs during colonizations and migrations, and if these events are geographically dependant (e.g. distribution of buckthorn) the likelihood of virulent genotypes spreading may be low. While the movement and migration patterns of the soybean aphid are becoming clearer (Michel et al. 2009; Bahlai et al. 2010), a lack of understanding on the genetics of virulence hinders the ability to accurately predict hostplant resistance sustainability.

6. Conclusion and future needs for biotype evolution As the discovery of soybean aphid biotypes occurred relatively recently, (Kim et al. 2008; Hill et al. 2010), much information is still needed to determine their influence on resistant soybean varieties. Most critical are determining the relative frequencies and distributions of

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biotypes, and further elucidating the role of the overwintering host in regards to population genetic issues. However, the largest obstacle remains individually diagnosing aphids to biotypes. Using detached leaves may help (Michel et al. 2010), but this is a laborious process (taking about 2 weeks) and may not work for all host-plant resistant varieties. For example, Michel et al. (2010) demonstrated that PI567301B, which shares similar qualities to Rag2 (PI243540, Mian et al. 2008), does not retain resistance when leaves are detached from whole plants. Ultimately, diagnosis should rely on molecular methods, but linking molecular markers to biotype designations may take many years and prove difficult in an aphid species where any such genetic markers may disassociate from the virulence trait through recombination in the obligatory sexual phase. Newly developed molecular resources for the soybean aphid should help in the search for diagnostic genetic markers. The question remains, however, as to how durable host-plant resistant soybean varieties against the soybean aphid will remain effective. In other host-plant resistance systems with virulent aphid biotypes, resistant varieties can remain durable for extended periods of time. For almost 100 years, resistance in grapes (Vitis vinifera) to Diuraphis noxia has held. For Schizaphis graminum, host plant resistance in wheat has been tenuous at best (Puterka and Peters 199). However, studies have suggested that other hosts instead of wheat (wild grasses) were driving biotype adaptation instead (Anstead et al. 2003). Since the soybean aphid has no known significant secondary host other than soybean, research on S. graminum may not be a completely accurate comparison, but it may forewarn users and researchers about the utility of host-plant resistance. The presence and evolution of soybean aphid biotypes places great importance on the constant development of new soybean aphid resistant soybean varieties (Hill et al. 2010). By having a wide array of potential soybean aphid resistant genes and varieties—including the possibility virulence can likely be managed and the durability of host-plant resistance will be preserved. In addition, the possibility of mixing or gene pyramiding (combining more than one resistance gene in a variety) and geographically varying Rag gene deployment may extend the life of host-plant resistance (Porter et al. 2000; Bush et al. 1991; Smith 2005). Furthermore it is important to keep in mind other potential control tactics including insecticidal seed treatments and mid or late season insecticide applications. Through a multi-year and multi-state research collaboration, an economic threshold exists for the soybean aphid (Ragsdale et al. 2007), which works for the majority of soybean fields. The integration of all tactics will be necessary to slow the evolution of soybean aphid biotypes and extend the durability of host-plant resistance in soybean.

7. Acknowledgements We would like to acknowledge W. Zhang, L. Orantes, Y. Chen, N. Davila-Olivas, L. Cañas, J. Todd, and L. Wallace for soybean aphid rearing and detached leaf assay tests. T. Mendiola. K. Freewalt and T.-H. Jun assisted with soybean rearing and assessing host plant resistance. C. Hill (University of Illinois) provided biotype 1 individuals. Soybean aphid from South Dakota and Kansas were collected and sent by Dr. K. Tillmon (South Dakota State University) and Dr. B. McCornack (Kansas State University). Images were provided by Dr. R. Hammond (The Ohio State University). Dr. B. Bonning (Iowa State University) provided comments regarding soybean aphid genomics. Funding was provided by The Ohio Soybean Council, and the Ohio Agricultural Research and Development Center, The Ohio State University.

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17 Evaluation and Utilization of Soybean Germplasm for Resistance to Cyst Nematode in China Ying-Hui Li, Xiao-Tian Qi, Ruzhen Chang and Li-Juan Qiu The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI) / Key Lab of Germplasm Utilization (MOA), Institute of Crop Science, Chinese Academy of Agricultural Sciences, 100081 Beijing, P. R. China 1. Introduction The total area of Soybean Cyst Nematode (SCN, Heterodera glycines Ichinohe)-infected soybean (Glycine max (L.) Merr.) in China increases each year, resulting in substantial yield losses. It becomes more and more important to discovery and incorporate new gene into breeding program for developing resistant cultivars. In this paper, we introduced more than 400 resistant accessions which were resistant to race 1, 2, 3, 4 and 5, especially some ones with resistance to multiple races. These resistant accessions were used to construct SCN applied core collection that consisted of 29 accessions but could represent more than 70% of genetic diversity of the whole resistant collection. By using linkage and association mapping methods, 15 QTLs, 22 SSR markers, 5 SNP markers and 1 SNP haplotype were identified associated with resistance to SCN. Moreover, one resistant candidate gene GmHs1pro-1 was cloned and analyzed for sequence variation. In addition, 11 SNP markers were developed from candidate genes rhg1 and Rhg4 respectively, which had been used to analysis genetic diversity and genetic relationship among soybean accessions. However, it is essential to discover novel gene from the resistant accession and explore new breeding methods for accelerating SCN resistant soybean improvement.

2. Background information SCN was one of the most destructive pests for soybean production and easily cause significant agricultural problem. Annual economic losses associated with SCN have been estimated to be as high as $120 million in China. It was firstly found in northeast China in 1899 and then in Korean, Japan, U.S.A, Columbia, Canada, Brazil, Argentina, Russia, and Indonesia and the other counties. Now it has infested the main soybean cultivation regions of China. The life cycle of SCN is divided into three stages: egg, larva, and dult. Larva, the major infection form, infects and damages soybean plants via mechanical injury, physiological damage and induction of infection by another pathogens. In 1952, Ichinohe defined SCN as a separate species, Heterodera glycines Ichinohe based on the detailed morphological study. Within the species, SCN is extremely variable genetically. Ross (1962) firstly noticed that the isolates of H. glycines from different places in U.S.A were different when they inoculated to the soybean

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Plant Introduction (PI) 88788, suggesting that the genetic diversity was existed among populations of the nematode. For developing a classification scheme that would separate the major genetic groups for host compatibility within H. glycines, a race test was proposed (Golden et al., 1970). Using four resistant (Peking, Pickett, PI90763 and PI88788) and one susceptible accessions (Lee) as differential host, a four-race scheme was established based on comparative development of females (Golden et al., 1970). Later, for meeting with the demands of adequately description the extensive genetic diversity of H. glycines populations that existed in soybean production areas, a 16-race identification system was developed taken the four-race system as the base (Riggs & Schmitt, 1988). Until now, 14 races had been discovered except for race 11 and 13 worldwide. For avoiding the implication of genetic uniformity or predictability and efficiently document and discuss population differences, HG Type scheme was also developed (Niblack et al., 2002) using seven differential hosts including PI58402 (Peking), PI88788, PI90763, PI437654, PI209332, PI89772 and PI548316). Since most of resistance accessions were identified using race designations and 16-race identification system was still comprehensively used in China, we still adopt 16-race identification system in this review. Nine races including race 1, 2, 3, 4, 5, 6, 7, 9 and 14 have been found (Liu, 2005) in China. Among them, race 1 was predominant in Shandong, Hebei, and Shaanxi; race 3 was predominant in the northeast (including Heilongjiang, Jilin, Liaoning and Inner Mongolia); and race 4 predominated in Shanxi and Beijing city (Wu et al., 1982; Liu et al., 1984 and 1989a; Shang et al., 1989; Li et al., 1991; Lu et al., 2006). The total area of SCN-infected soybean increases each year, resulting in substantial yield losses. The controlling SCN methods include planting resistant cultivars, rotation with no host crops and bio-prevention with microorganism. Although no completely efficient way has been worked out for the SCN control, developing and planting of resistant cultivars is the most cost-effective and environmentally friendly method of controlling SCN.

3. Major issues Resistance accessions to SCN had been screened worldwide, especially in USA and China. Over 100 resistance accessions were identified from plant introductions from the USDA Soybean Germplasm Collection (Chen et al., 2006), including PI 88788, Peking, PI 437654 etc. But 432 resistance accessions from Chinese soybean germplam (Ma et al., 2006), including Huipizhiheidou, Wuzaiheidou, Yingxianxiaoheidou, Harbinxiaoheidou etc, which were identified from less than 16000 accessions that taking 70% of the total Chinese soybean accessions. Only few of them have been used to discover resistance genes or QTLs. Until now, three recessive (rhg1, rhg2 and rhg3) and two dominant genes (Rhg4 and Rhg5) have been reported (Caldwell et al., 1960; Matson & Williams, 1965; Rao-Arelli, 1994), and 151 QTL have been mapped from several accessions since 1994 (Zhang et al., 2010). Among them, one SCN locus refer to the resistant gene rhg1 was repeatedly mapped in almost all resistance accessions. The accessions with rhg1 were typically found to confer the greatest SCN resistance of any of the resistance QTLs. The Glyma18g02680.1 gene at the Rhg1 locus that encodes an apparent leucine-rich repeat transmembrane receptor-kinase (LRR-kinase). However, introduction of the gene (genomic promoter/coding region/terminator; Peking/PI 437654derived SCN-resistant source) into rhg1-SCN-susceptible plant lines carrying the resistantsource Rhg4+locus, provided no significant increases in SCN resistance. Studies in which expression of the LRR-kinase gene from different resistance accessions was either reduced or complemented did not reveal significant impacts on SCN resistance, suggesting that one or

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more other genes at the Rhg1 locus may be the primary determinant of differences in SCN resistance among different soybean types (Melito et al., 2010). However, one SNP haplotype and one InDeal markers developed from this genes were identified related to resistance and could be used (combine with Satt309) in the marker assistant selection (MAS) (Li et al., 2009; Nan et al., 2009). Therefore, it becomes more and more important to discover and incorporate map-based cloned gene in breeding program for developing resistant cultivars. The narrow genetic base of SCN resistance is more apparent since the few resistant accessions used. Using SSR markers, Chen et al. (2006) clustered resistance accessions identified in USA into 7 clusters, which accounted for 42% of the total variation. Five of the 7 clusters seem to represent 5 distinct groups of resistance accessions to different SCN races. Moreover, the differences among clusters and among individual lines of different clusters were significant showed by the result of AMOVA. However, only few resistance accessions have been used extensively in the development of SCN-resistant cultivars. For example, in Midwest of USA, more than 90% of SCN resistance cultivars derived from only one source, PI 88788 (Concibido et al., 2004). Moreover, resistance to SCN is a multigenic trait, continuously growing the same resistant cultivar(s) may play a selection pressure on SCN that result in SCN to overcome resistance and shift to “resistance-breaking” races, such that resistant accessions may express resistance through mechanisms conferred by several different genes or alleles that render the cultivars susceptible (Riggs & Schmidt 1988). Therefore, increasing available genetic diversity for SCN resistance in prior is critical for the long-term stability of host plant resistance as a control strategy for SCN. The major issues, including the shortage of resistant accessions and genes, complex of resistant mechanism and indefinite genetic base of resistant cultivars retard the development of the SCN breeding. A search for novel resistance accessions and novel resistance genes as well as new utilization and breeding methods based on “broad-based resistance” has become a major objective in soybean research. It is generally accepted that soybean originated in China and abundant resistant accessions were resided in the Chinese gene pool. Soybean cultivation in China can be traced back to the ancient Huangdi period, as evidenced by the ancient books and archaeological discoveries. During the 4500 year planting history, a lot of soybean germplasm which exhibited highly phenotypic diversity and genetic diversity as a result of adaptation to complex climate and soil environments, as well as the human need were developed in China. Some of germplasm have been introduced into other major soybean-producing countries including the United States, Brazil, and Japan. These germplasm have emerged as valuable accessions for breeding SCN resistance cultivars. Niblack et al. (2002) assumed many “landraces” of soybean carried resistance to H. glycines, including populations that were genetically diverse since Chinese soybean farmers always selected soybean plants that provided acceptable levels of production in soils with a problem caused by H. glycines. Therefore, we introduced the progress on discovery and utilization of resistant accession in China in order to benefit the global soybean society for soybean improvement and production.

4. Progress on genetics and breeding 4.1 Survey of SCN races in China SCN was mainly discovered in the two principal soybean production areas in China, Northeast and Huanghuai-Hai region (Table 1). Jaczevski firstly found SCN in the west part of Heilongjiang province in the northeast China in 1899. Until 1987, almost all regions in

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Soybean - Molecular Aspects of Breeding

Heilongjiang province had the SCN problems except the five counties (Fuyuan, Huma, etc.). Among the SCN samples collected from 65 counties in Heilongjiang province, three races were identified using 16-race identification system. Race 3 was found in 63 samples, race 6 and race 7 were only in one sample respectively, suggesting that race 3 was the predominant race in Heilongjiang province (Liu et al., 1995). Except theses three races, race 1, 4, 5 and 14 were also discovered in Heilongjiang, whereas race 3 was still predominant (Shang & Liu, 1989; Dong et al., 2008). Based on the degree of SCN occurrence, Heilongjiang was divided into three regions, in which the west region was severe infected area, the east (Three River Plain) was medium infected area and the rest area was light infected area (Liu et al., 1987). Within Jinlin province, Race 1 and 3 were predominant and race 5 and 7 were also discovered (Liu et al., 1992; Shang and Liu, 1989). Compared to Heilongjiang, Jilin and Inner Mengolia, the distribution pattern of SCN in Liaoning was much simple, there are only race 1 and 3 (Liu et al., 1985; Shang & Liu, 1989). Within the Huanghuai-Hai region in China, race 4 and 5 were firstly identified in the north region of Huai river (Zhang, 1988; Zhang et al., 1990), following by the race 4 in Beijing (Yan et al., 1995) and the race 7 in Shandong and Henan (Chen et al., 1988). In order to illustrate the major distribution pattern, a map of SCN races survey for a total of 38 sites in HuangHuai valleys was constructed (Lu et al., 2006). Three areas mainly infested with race 1 were identified, i.e. the area south-east to Jinan in Shangdong province, the area of northern Henan province and its border region to south of Hebei province, and the area of Luohe, Zhoukou of Henan province and Fuyang of Anhui province. Race 4 was predominant in Shanxi province, Beijing and the adjacent area of Henan, Shandong and Anhui provinces, and the delta of Huanghe River in Shandong province. Race 2 was mainly in Liaocheng, Dezhou of Shangdong province and Shijiazhuang of Hebei province, and Jiaozuo and Huojia of Henan province. Race 7 distributed in the west part of Jiaodong Peninsula of Shandong province and Kaifeng, Huaxian, Wenxian of Henan province. Race 5 was found and scattered in Hebei and Henan province. In this study, race 9 which was reported the first time in China was found in Shangqiu of Henan province. From the map of race distribution, race 1 and race 4 were the two predominant races in Huang-Huai regions (Lu et al., 2006). During 1992-1995, 12 immune and 28 resistant accessions to race 3 of SCN were evaluated by both natural infection in the field of different provinces and artificial inoculation. Among these accessions, only one accession shafted its phenotype from immune to resistant, confirming durable and stable resistance (Ma et al., 1996). However, the new SCN races were generated from the original races after forcibly propagation on resistant varieties for 912 generations reported in several previous studies. After retested on the differential hosts of soybean cyst nematode, the populations of the race 1 was acted as race 4 on Peking and Franklin, race 5 on Xiaolihei, race 6 on Tiefeng 18, race 9 on Fayette and Xiao li Black (Liu et al., 1998). The population of the race 3 was acted as race 2 on Harbinxiaoheidou, race 4 on Changlihei, race 14 on Peking, race 13 on Franklin while still as race 3 on Lee 68 (Liu et al., 1993). Recently, Yu et al. (2009) reported that the original SCN race 3 populations changed to race 6 after 10 generations of continuous planting resistant cultivars on Kangxian 1, Kangxian 2, Kangxian 3, Kangxian 4, and Kangxian 5; to race 10 on Huipizhiheidou; to race 14 on Peking; and to race 15 on Harbinxiaoheidou. Under production condition, the population of SCN race 3 was variable by planting the resistant soybeans for 13 years. According to Riggs & Schmitt (1988) standard, the changed race was race 4

Evaluation and Utilization of Soybean Germplasm for Resistance to Cyst Nematode in China

Race Region Race 1 Northeast

Province Jilin

Liaoning

HuanghuaiHai

Hebei Henan

Race 2 HuanghuaiHai

Jiangsu Anhui Shandong Hebei Henan Shangdong

Race 3 Northeast

Heilongjiang

Jilin

City or County Chuangtu, Kangping, Heishan, Zhangwu, Anshan, Jinxian, Lvshun, Yixian, Taian etc.

Reference Shang and Liu, 1989

Changtu

Liu and Liu, 1989 Liu et al., 1989

Handan

Lu et al., 2006

Yixian, lvshun

Huaxian, Heping, Lizhuang, Xuchang, Linying Xuzhou Fuyang Jinan

377

Lu et al., 2006 Lu et al., 2006 Lu et al., 2006 Lu et al., 2006

Gaocheng

Lu et al., 2006

Jiaozuo, Huojia

Lu et al., 2006 Zheng and Yan, 1997

Dezhou Heihe, Tonghe, Yichun, Anda, Fujin, Jiamusi, Sunwu, Boli, Suihua, Baiquan, Huachuan, Hailin, Jiayin, Xuke, Airport of Harbin, Beian, Wudalianchi, Shuangcheng, Kedong, Suileng, Hailun

Dong et al., 2008

Fuyu

Liu and Liu, 1989

Harbin, Jiamusi, Mudanjiang, Zhaodong, Tieli Jiamusi, Harbin, Mudanjiang, Zhaodong, Tieli, Xinxun, Mingshui, Daqing, Baoqing, Suihua, Jixian, Bingxian, Beian, Kedong, Gannan, Fangzheng, Anda, Yaohe, Shangzhi, Huacuan, Mulan, Lanxi, Jidong, Fuyu, Linkou, Mishan, Nengjiang, Keshan, Tonghe Baicheng, Zhenlai, Daan, Fuyu, Zhaonan, Nongan, Changling, Yushu, Panshi, Jingyu

Liu et al., 1985

Liu et al., 1989b

Liu et al., 1992

378 Race

Soybean - Molecular Aspects of Breeding

Region

Province Liaoning Mengolia

Race 4 Northeast HuanghuaiHai

Heilongjiang Anhui

Beijing Henan Shandong Shanxi

City or County Shenyang, Fushun, Kaiyuan, Fengcheng

Anda

Reference Liu and Liu, 1989 Liu and Liu, 1989 Dong et al., 2008

Tangshan

Liu et al., 1989

Tangshan, Haoxian, Suixi Changping Puyang, Zhengzhou, Tongxu, Heze Taigu Fanshi, Wuxiang

Zhang, 1988 Yan et al., 1995 Lu et al., 2006 Lu et al., 2006 Liu et al. 1989 Chen et al., 2001 Liu and Liu, 1989 Lu et al., 2006 Liu et al. 1989 Chen et al., 2001 Chen et al., 2001 Chen et al., 2001 Shang and Liu, 1989

Kezuoyouqi

Taiyuan Shangdong

Race 5 Northeast HuanghuaiHai

Henan Jiangsu Jilin Anhui

Hebei Henan Shandong Race 6 Northeast Race 7 Northeast

Heilongjiang Mengolia Jilin

Fenyang, Linfen Kenli Heze Shangqiu Peixian Tongyu Mengcheng

Zhang, 1988

Mengcheng Mengcheng Changxian Kaifeng, Huaxian, Wenxian Changge, Dazhou, Dongcun Jiaoxian, Weifang, Gaomi, Changyi Dedu

Zhang et al., 1990 Lu et al., 2006 Lu et al., 2006 Chen et al., 2001 Lu et al., 2006

Changling, Qianguo Changling, Qianguo

Helongjiang Henan Shandong Race 9 HuanghuaiHai Race Northeast 14

Henan Heilongjiang

Qinggang Kaifeng, Huaxian, Wenxian Jiaoxian, Weifang, Gaomi, Changyi

Chen et al., 2001 Liu et al., 1995 Chen et al., 1987 Liu et al., 1992 Shang and Liu, 1989 Liu et al., 1995 Chen et al., 1987 Chen et al., 1987

Shangqiu

Lu et al., 2006

Anda

Dong et al., 2008

Table 1. The distribution pattern of SCN in China

Evaluation and Utilization of Soybean Germplasm for Resistance to Cyst Nematode in China

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and race 14, despite the race 4 did not find in Heilongjiang province before 2007 (Tian et al., 2007). These changes of races indicated that there might exist other races in an area with some predominant races since new SCN races were easily generated after long term of resistant varieties planting. These new races make resistant varieties become susceptible. For decreasing the damage of SCN, the study conducted in the soybean field under continuous cropping (CS), wheat-soybean-wheat-soybean (WSWS) and wheat-maize-soybean (WMS) rotation systems in Heilongjiang. Total of 8 orders, 19 family and 43 genera nematodes were identified. Among them, 14 genera were affected by the crops stubbles and soil environment. Total number of soil nematodes was exhibited trends from low to high to low in CS and continuous higher in WSWS and WMS (Han et al., 2008). H. glycines would be acted as key species among all types of nematodes. The number of SCN including the number of cysts and all juveniles of different stages in soil and roots of soybean was notably increased in continuous cropping soybean field against rotation (Chen et al., 2007) because rotation can effectively decrease the SCN and other diseases in the soil. 4.2 Survey of SCN resistant accessions in China Except the rotation with nonhost crops, planting resistant cultivars was another primary method for controlling SCN (Concibido et al., 2004). There SCN resistant accessions identification was beginning since the late seventies of last century in China. Wu et al. (1982) firstly reported four screened resistant accessions including Longkang SCN781, 782, 791 and 792 from 532 cultivated soybeans, 162 semi-wild soybeans and 115 wild soybeans by growing soybeans in pots with SCN infested soil. 11 varieties highly resistance to SCN were identified from 471 soybean accessions using field identification methods (Xu et al., 2007). Liu et al. (1989a) screened 13 resistant accessions without race specific identification from more than 1000 soybean varieties, then further discovered 12 accessions resistant to race 1, 13 accessions resistant to race 3 and 1 accession resistant to race 4 (Liu et al., 1989). Since then, a set of resistant accessions have been identified focused on the predominant races on the different mainly production areas. Race 1 mainly distributed in Liaoning, Jilin, Henan and Shandong provinces. A total of 7772 soybean varieties form Huanghuai-Hai region, including Henan and Shandong province were firstly screened in the field infested with SCN and obtained 8 immune and 69 resistant accessions (Liu et al., 1991). Then a total of 10,117 accessions from different production regions were screened by Coordinative Group of Evaluation of SCN (1993) and 128 resistant and 16 immune accessions were identified. Among the 16 immune accessions, 13 were from Huanghuai-Hai region, including 9 from Shanxi, 2 from Hebei, 1 from Shandong and 1 from Shaanxi province. Moreover, all of 16 immune and 77 (68.8% of total) resistant accessions were with black seedcoat. For race 2, 4 and 21 accessions were identified as immune and resistant respectively (Zheng & Yan, 1997). For race 3, more than 10000 original landraces have been screened. A total of 88 local soybean varieties collected mainly from Liaoning province were screened by planting in the SCN infested plots and pots with infested soil. Of them, 17 resistant varieties to race 3 were identified (Liu & Liu, 1985). Then 8183 cultivated soybean accessions were evaluated for their resistance to race 3 of SCN by both SCN infected soil and pot accessions during 1986 to 1990. Among them, 29 accessions showed immunity and 209 varieties showed resistant (Ma et al., 1991). Moreover Xu (1992) identified 48 accessions resistant from 1991 accessions. Although these studies identified a set of resistant accessions, the identification methods were not standard. Therefore, Coordinative Group of Evaluation

380

Soybean - Molecular Aspects of Breeding

of SCN was constructed on 1986. More than 10167 accessions in China were evaluated for their resistance to the races 1, 3, and 4 of SCN, by growing them in the SCN infested soil and counting the number of white female cyst on the roots 5-6 weeks after planting. Accessions resistant and 30 accessions immune were identified (Coordinative Group of Evaluation of SCN, 1993). It was worth noting that all of immune accessions were from Huanghuai-Hai region. Ma et al. (1996) also identified 5 immune and 19 resistant accessions from 3355 cultivars. Of them, no immune and resistant accession was from South China. Li et al. (2008) also identified 8 resistant accessions from 394 soybean accessions from 7 provinces and cities during 2003 to 2006. Except for landraces, elite cultivars or lines were also screened. 36 released soybean cultivars from different locations of Heilongjiang province were evaluated for their resistance to race 3 of SCN during 1996 to 2000. The results showed that, 2 cultivars were resistant (Yu et al., 2001). And recently 7 lines resistant to SCN were identified from 71 new soybean elite lines (Yu and Wang, 2004). Race 4 of SCN was mainly occurred and damaged the soybean production in HuanghuaiHai region. 11 highly resistant and 1 moderately resistant varieties to race 4 were first identified from 1920 soybean accessions, mainly landraces from Shanxi province (Li et al., 1987). Later, Li et al. (1991) screened 8240 soybean germplasms from six large areas of China and identified 9 high resistant accessions from Shanxi, Hebei and Shaanxi provinces. Zhang and Dai (1991) also identified seven resistant accessions to race 4 from 1800 soybean accessions from Huang-Huai area. Recently, Wang et al. (2005) screened 518 elite cultivar or lines and identified three resistant cultivars or lines. Coordinative Group of Evaluation of SCN (1993) also evaluated 10031 soybean accessions and identified 11 resistant accessions. However no immune materials were discovered until now. Race 5 was mainly distributed in Henan, Shandong and Anhui province in Huanghuai-Hai region. Zhang & Dai (1992) firstly screened 907 soybean accessions from Huanghuai-Hai region and identified seven accessions resistant to race 5. Then, five accessions resistant to race 5 were identified from 904 soybean germplasms collected from Anhui province in Huanghuai-Hai region (Zhang, 1995). During 1992-1995, 3391 accessions from 21 provinces or cities in China were evaluated. Among the germplasms evaluated, 27 accessions showed resistant, it was 0.80% of the total evaluation number of accessions. Most of accessions resistant to soybean cyst nematode came from Hebei, Shandong, Shanxi, Chifeng, Jilin and Heilongjiang (Zhang et al., 1998). Besides these identification focused on regional predominant races, some studies pay their attention on discovering the accessions resistant to multiple races. Liu et al. (1987) evaluated a total of 202 local soybean varieties collected mainly from Northeast China and identified four varieties resistant to both race 1 and 3 (Liu et al., 1987). Fu et al. (1989) evaluated ten resistant elite lines primary identified. Among them, 7 elite lines resistant to both race 3 and 4 and 3 were resistant to race 1, 3 and 4. Coordinative Group of Evaluation of SCN (1993) identified 4 accessions were resistant to the race1, 3, and 4 of SCN, including Wuzaiheidou, Huipizhiheidou, Longyaodaheidou and Wubaoheiheidou. Until now, a total of 432 SCN resistant germplasm were summarized based on the previous studies. These accessions came from 18 provinces, cities and autonomous regions (Table 2; Fig. 1a). The most of resistant accession (89.35%) were from Huanghuai-Hai region. Among the provinces in Huanghuai-Hai region, Shanxi and Hebei ranked the top, taking 42.59% and 22.45% of the total resistant accessions respectively. Among the resistant accessions, most of them were resistant to race 1 and race 3, taking 33.1% and 66.7%, a few accessions were

Evaluation and Utilization of Soybean Germplasm for Resistance to Cyst Nematode in China

381

resistant to race 2, race 4 and race 5, taking 6.7%, 3.4% and 4.4% respectively; 89.8% accessions were landraces, indicating Chinese landraces was the large gene pool for identification of resistance to SCN.

Origin

No. of immunity and high resistant accessions Total

0

5

29

6.71

3 3 10 0

0 0 0 0

2 3 0 0

8 7 11 3

1.85 1.62 2.55 0.69

19

272

15

14

386

89.35

1 2

6 0

3 4

2 0

0 0

12 4

2.78 0.93

Gansu

1

0

3

0

0

4

0.93

Hebei Henan Jiangsu Inner Mongolia

36

9

79

1

0

97

22.45

9 4 3

0 3 0

7 0 7

0 0 0

0 0 0

14 7 9

3.24 1.62 2.08

Shanxi

41

1

128

12

9

184

42.59

Shaanxi Shandong

11

0

21

0

0

27

6.25

11 15

0 5

20 0

0 0

5 0

28 17

6.48 3.94

6 9 0 0

0 3 1 1

0 0 0 0

0 0 0 0

0 0 0 0

6 9 1 1

1.39 2.08 0.23 0.23

South China Guizhou Hubei Hunan Jiangxi

Race 2

Race 3 Race 4

9

5

16

3 0 3 3

3 1 1 0

119

Ratio (%)

Race 5

Northeast China Heilongjiang Jilin Liaoning Inner Mongolia Huanghuai-Hai region Anhui Beijing

Race 1

Table 2. The distribution pattern of Chinese soybean germplasm resistant to SCN Since most of cultivated resistant accessions had one or more primary evolutionary traits, such as small seed, dark seed color, and viny growth habit etc, more number of resistant accessions were expected to be identified from wild soybeans. However, the frequency of resistant accessions in wild soybeans was much lower than in landraces. Wu et al. (1982) screened 162 semi-wild and 115 wild soybeans but no resistant source was discovered. Ma et al. (1996) also could not discover any accessions resistant to race 3 of SCN from 186 wild soybean accessions. Only Zhang et al. (1998) identified 3 accessions resistant to race 5 from 169 wild soybean accessions, mainly from Heilongjiang (130) and Beijing (21). It was 1. 78% of the total evaluation number of wild soybean. It was worth noting that all of them were from Heilongjiang province although no race 5 was discovered in this province.

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Soybean - Molecular Aspects of Breeding

3 1 3 2

1

11 2 1 1

2 1 1

Fig. 1. The distribution of 432 resistant accessions (a) and Chinese SCN core collection (b). The number of resistant accessions in b displays as blue letter.

Evaluation and Utilization of Soybean Germplasm for Resistance to Cyst Nematode in China

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4.3 Establishing applied core collection of resistance to SCN China has the most soybean germplasm in the world, but only about 1% of them have been used in soybean breeding program, which results in narrow genetic base for developing varieties (Qiu et al., 2009). By using four resistant and one susceptible standard soybean genotypes as differential lines (Golden et al. 1970; Riggs & Schmitt, 1988), more than 400 resistant germplasm accessions have been identified since 1985, but a few of them has been analyzed at molecular level. In order to efficiently use Chinese SCN resistant accessions, the concept of core collection proposed by Frank and Brown (1984) was adopted. The primary applied core collection for SCN resistant was first constructed based on 9 quantitative traits, 5 qualitative traits and molecular marker analyses. The high resistant accessions were randomly selected within each group based on agronomic traits cluster analyses, but the accessions resistances to more SCN races were priority to be selected. This primary core collection consisted of higher ratio of accessions resistance to race 5 (100%), race 2 (92.5%) and race 4 (80%) due to their low frequencies in the total, medium ratio (53.57%) to race 1, but the lowest (19.77％) to race 3. And the accessions except primary applied core collection were named as reserved collection. When compared to the whole collection, the primary core collection showed relatively high coincidence on the changing range of variation, mean value, of 5 qualitative traits including growth period (d), plant height (cm), 100 seed weight (g), protein content (%) and fat content (%). To compare the coincidence between primary core collection and the reserved collection, two set of 14 accessions from each of the reserved collection and primary applied core collection were analyzed with 50 SSR loci. 157 and 182 alleles were observed for each of the reserved and primary applied collections. The shared alleles between two collection were 141, indicating that primary applied core collection could represent 89.8% of the whole collection. Therefore this primary applied core collection for SCN resistance composed of 149 accessions could be better represent the whole resistant accession (Ma et al., 2006). For establishing core collection, five selecting methods were compared based on the SSR genotyping data from 50 SSR loci, which approximately uniformly distributed on 20 soybean genetic linkage groups. (1) Random selection (RS): the samples were random selected without any relationship between different sampling ratios. (2) Containing random selection (CRS): After the first sample is completely random selected, it was used for further random selection. (3) Phenotype clustering selection (PCS): The selection was carried out based on the agronomic traits cluster analyses. (4) Resistant stratify clustering selection (RSCS): After stratify by resistant level, the selection was carried out within group based on SSR data. (5) SSR clustering selection (SCS): The selection was determined after cluster analyses based on the SSR data. Among five selection methods, both random methods of RS and CRS had lower reserved ratio of alleles than the other 3 stratifying methods of PCS, RSCS and SCS, in which RSCS and SCS were better than PCS. When sampling ratio was 27％, allele reserved ratios for RSCS and SCS were more than 85%, but not for the other 3 methods. When the sampling ratio was 20％, RSCS and SCS had about 80％ allele reserved ratio, but decrease to about 65％ as the sampling ratio down to the 10％. 19 accessions were first selected since they were the same when compared RSCS to SCS, which taking 67.5％ of the total alleles. Another 10 accessions were added to core collection after comprehensive comparison of similarity coefficients, agronomic traits, and resistance. Finally the SCN applied core collection consisted of 29 resistant accessions, which takes 78.06% of the total primary alleles (392) and represent 70.11% of diversity of the total collection. All 29 accessions consisted of 17 immune and 12 high resistant materials, which included 4

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accessions resistant to 3 SCN races and 4 SCN accessions resistant to 2 SCN races and the rest 21 accessions resistant to only 1 SCN race. Among them, 20 were landraces and the rest were bred lines. They were collected from 12 provinces of Heilongjiang (3), Jilin (1), Hebei (3), Shanxi (11), Shaanxi (2), Shandong (2), Jiangsu (1), Jiangxi (1), Anhui (1), Hubei (2), Hunan (1) and Guizhou (1) (Fig. 1b) (Ma et al., 2006). 4.4 Cloning and expression of candidate resistant genes We know little about the SCN resistant mechanism for the complex interactions between soybean–nematode. The best-studied defence mechanism is based on the gene-for-gene resistance model through which plants with specific resistance genes specifically recognize pathogen genotypes carrying matching avirulence genes. Until now, Rhg4 and rhg1, the two major genes conferring resistance to SCN in soybean, were cloned (Meksem et al. 2005; Ruben et al. 2006). The Hs1pro-1 gene for resistance to the beet cyst nematode in sugar beet was expected to have resistant function to SCN in soybean. Therefore, the Beta procumbens Hs1pro-1 sequence (GenBank accession no. AAB48305.1) was used as a query probe to search the EST database at TIGR through the tblastn program, and an EST (TC219197) with substantial sequence similarity was obtained. According to the EST, a fragment which is 1,477 bp with a full-length ORF of 1,314 bp was obtained and deposited in GenBank as GmHs1pro-1. The GmHs1pro-1, encodes a protein of 437 amino acid residues with Hspro1_N and Hspro1_C domains related to resistance to nematodes. Database searches revealed that GmHs1pro-1, has 49% identity and 68% similarity with AKINbetagamma-interacting protein 1 from Arabidopsis thaliana, and 49%identity and 68%similarity with Hs1pro-1 from Beta procumbens over a stretch of 258 amino acids (189–432 in GmHs1pro-1) (Yuan et al., 2008). Taking actin primers as control for the presence of equal amounts of cDNA, RT-PCR analysis revealed GmHs1pro-1 mRNA is more abundant in root, stem, and young leaf than it is in flower, pod, and old leaf. In pod, GmHs1pro-1 mRNA was almost undetectable (Yuan et al. 2008). Then the expression characteristics of candidate gene GmHslpro-1 on the resistant line Zhongpin03-5373 and susceptible cultivar Zhonghuang13 were performed using the realtime PCR technique to understand the relationship between the gene and resistance to SCN. The relative expression of the gene raised after 1d vaccinating SCN race 4 (SCN 4) and it was 13.4 times higher than that of the control sample, the subsequent time points kept the upward and showed weakened after inoculated 6d. Analysis of variance showed that the relative expression of the gene was significant difference between inoculated and uninoculated at significant difference (0.05 or 0.01 level). It indicated GmHslpro-1 might be induced to express in the conditions of SCN 4 infection (unpublished). Lu & Fang (2003) inoculated 2-day-old soybean seedlings of Huipizhiheidou with J2 of SCN race 4. Thirty six cDNA clones corresponding to mRNAs with different abundances in SCN - infected (19 cDNAs) and uninfected (17 cDNAs) roots were identified using mRNA differential display technique. Six of them were recovered as plasmid clones that showed hybridization intensity differences in reverse dot blotting assay. These clones were discovered from soybean roots after inoculation. Their sequences had been registered in GenBank and were subjected to database comparisons with the aid of BLAST algorithm. Of then, A32 clone showed strong similarity to EST cDNA for coding MYB of Arabidopsis late elongated hypocotyls protein, one of cDNA from the library induced by tomato root nutrition deficiency and tomato cDNA of pseudomonas resistance expression library.

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4.5 Genetic diversity of resistant germplasm 4.5.1 Molecular markers diversity To illustrate the distribution of alleles at SNPs loci in Chinese accessions and provides the information for utilization of SCN resistant accessions, 57 resistant soybean collections from both China and US were analyzed using SNPs at rhg1 and Rhg4 loci by using single based chain extension with a microsphere based assay. At both rhg1 and Rhg4 loci, 7 of 9 US resistant accessions and 37 of 49 Chinese resistant accessions had resistant genotypes too, taking 77.8% and 66.7%of the total accessions, respectively (Qiu et al., 2003). Satt309 was the most closely linked (0.4 cM) marker to rhg1, the most important gene resistant to SCN and usually used in the molecular marker assistant selection. Wang et al. (2003) analyzed 149 SCN immune or high resistant accessions, 495 susceptible accessions and 21 introductions from U. S.A using this SSR marker. A total of 6 alleles were identified, and found 1 novel allele between 134bp and 149bp. The two accessions carried this novel allele were immune and moderate resistance to race 1 separately. Most susceptible accessions, taking 92.13% of the total, had one of 125bp, 134bp and 149bp alleles; while most immune or multi-resistant accessions were identified to have 128bp or 134bp allele. 14 of 16 accessions with 128bp allele came from Shanxi province, which suggested that Shanxi province maybe was the origin of accessions with 128bp allele. 4.5.2 SNP discovery and diversity analysis of candidate resistant genes By sequencing SCN related gene GmHs1pro-1 of 8 landraces, 12 cultivars and 23 wild soybeans in addition to WF7, fourteen single-nucleotide polymorphisms (SNPs) were detected, among which 3 were singletons and 11 were informative sites, resulting one SNP every 105.5 bp. Of the 14 SNPs, 6 were nonsynonymous (the encoded amino acid was altered by the SNP), 6 were synonymous (the encoded amino acid was not altered by the SNP), and 2 occurred in the 3’-untranslated region (3’-UTR). Six nucleotide mutation types (T/A, C/A, C/T, T/G, G/A, G/C) were detected; eight were involved in transitions, and six were transversions with a transition- transversion ratio of 4/3. Of 11 SNPs in wild soybeans, five were not found in landraces or cultivars, nine of three SNPs in landraces were unique, whereas there was none for three SNPs in cultivated soybean. Two parameters of Π and θ values were calculated to describe sequence diversity of GmHs1pro-1, which were 0.00168 and 0.00218 among 43 accessions. Among the three populations, landraces had the highest diversity with π = 0.00249 and θ = 0.00235, followed by wild soybeans, while cultivars had the lowest diversity with π = 0.00031 and θ = 0.00065 (Yuan et al., 2008). The sequence of major SCN candidated gene rhg1 were compared in aligned 4995 nucleotides. A total of 42 polymorphisms among eight soybean genotypes consisted of 37 SNPs and 5 InDels. Among the 37 SNPs, 11 (29.7%) were singleton variable loci that found in only one genotype, and 26 (70.3%) were parsimony informative loci found in at least two genotypes. No SNPs were detected in the 5’-noncoding region, nine were in exon1, one in the intron, two in exon2 and 25 in the 3’-noncoding region. Seven of the 11 SNPs in the coding regions led to changes in amino acid codons (six in exon1 and one in exon2). Moreover, more than 30 SNPs were detected from the sequence comparison of Chinese soybean accessions (Li et al., 2009). A total of 44 single nucleotide polymorphisms (SNPs) including 43 single base changes and 1 DNA insertion-deletions (Indels) were identified from Rhg4 sequence. Sequence diversity analysis on 24 Chinese cultivated and wild soybean showed π = 0.00203 and θ = 0.00348 for

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Rhg4. Comparison with two G. max populations of landraces and cultivars, G. max had the highest diversity (π = 0.00218 and θ = 0.00283) (Yuan et al., 2007). These SNPs from rhg1 and Rhg4 were used to clarify the difference of genetic diversity and linkage disequilibrium (LD) level between G. max and G. soja (Li et al., 2009). The results indicated that G. max population, consisted of cultivated soybean mini-core collection and modern cultivars, had a higher LD levels (R2 value is 0.216) than G. soja population. Within G. max population, two specific LD regions were formed for each gene. 4.6 SNP markers development and utilization Six AS-PCR (Agarose gel-based Co-dominant Allele-Specific) markers and 5 FLDAS (Fragment Length Discrepant Allele Specific) PCR markers were developed from rhg1 and Rhg4 respectively (Li et al., 2009; Yuan et al., 2007). All of them behaved co-dominantly except locus of 2564G>A that was dominant. The Actin gene (sequence number in GenBank: V00450) was used as a positive control in the multiplex PCR. Among the six selected SNPs for developing AS-PCR marker in rhg1, three were in exon1, two were in exon2 and one was in the 3’-noncoding region. Of the five SNPs in the coding regions, two (689C>A and 757C>T) were located between the N-terminal signal peptide domain and the leucine rich repeat domain; the others were located in the serine/threonine kinase domain. Four SNPs (at positions 689, 757, 2564 and 3995) form one haplotype present in five resistant genotypes (Peking, PI437654, Sangutiaoheidou, Xiaolimoshidou and Huipizhiheidou), and another haplotype in the susceptible genotypes Suinong 14 and Guxin. The SNP at position 2233 characterized all resistant genotypes except Huipizhiheidou. Finally, the SNP at position 2868 was present in all genotypes except You1298, the line with moderate resistance only to race5. The six SNPs from rhg1 formed nine distinct haplotypes among the 70 genotypes. The haplotypes (HAPs) from different cultivars were displayed in a neighbor-joining tree. Genotypes with HAP 1 to HAP 6 clustered in one group, and HAP 7 to HAP 9 in another, supported by a bootstrap value of 84%. The haplotype in G. soja and G. max was the same as that in several other SCN susceptible landraces and cultivars. All known resistant genotypes (23) were included in the first cluster, while the eight susceptible genotypes were all placed in the second cluster. The frequency of different haplotypes ranged from 1.4% to 24.3%. Of the nine haplotypes, HAP9 (A-T-A-A-T-C) was the most common and HAP7 (A-T-G-G-C-A) was unique being only present in Lee. The nucleotide diversity for the six SNP loci among the 70 genotypes was 0.449 and haplotype diversity was 0.841. The number of haplotypes detected was much less than 64 haplotypes as expected when assuming that the six loci are in Hardy-Weinberg equilibrium with each other. This may indicate that natural selection or directional selection for resistance to SCN has been occurring. Using 5 FLDAS makrers, the diversity of Rhg4 and its distribution in soybean germplasms was illustrated (Yuan et al., 2007). Rhg4 had high diversity, and higher diversity was found in G. max than that in G. soja. Distribution of the 5 SNP sites mentioned above between resistant and susceptible accessions to SCN indicated that Rhg4 takes a role in soybean resistance to the race1, 3 and 4 of SCN. 4.7 SCN genetic analysis and QTL discovery 4.7.1 Genetic linkage mapping Six resistant accessions were used to clarify the genetic mechanism of resistance and discover QTL resistant to SCN since 1992 (Table 3). Wu et al. (1992) crossed the SCN

Evaluation and Utilization of Soybean Germplasm for Resistance to Cyst Nematode in China

Cross

Hefeng25×Kangxian2*

Type of Race population F2

3

Locus

-

Marker or region Satt163-Satt309 Sat440-Satt148

Liaodou10×Xiaoliheidou*

Essex×Huipizhiheidou*

Jindou23×Huipizhiheidou*

F2

Reference Yang et al. (2010)

1

-

Satt187

Wang et al. (2007)

3

-

S11700

Wang et al. (2005)

1

rhgR1-1

Sat_210Sat_168

1

rhgR1-2

Sat_168Sat_141

1

rhgR1-3 Satt672-Satt413

4

rhgR4-1

Satt275Sat_210

4

rhgR4-2

Sat_168Sat_141

4

rhgR4-3

Satt442Sat_401

4

rhgR4-4

Sat_334Satt181

rhgR4a1

BSC-Satt632

rhgR4a2

Satt632Sat_162

F2

BC1

387

4

Lu et al. (2006)

Meng et al. (2003)

rhgR4aSatt610-Satt309 3 Yingxianxiaoheidou*×Jindou23

F2

4

-

Satt038, ISSR81

Fang et al. (2002)

Table 3. List of crosses and identified molecular markers associated with SCN reported resistant soybean with susceptible cultivars. They discovered all of F1 plants were susceptible in populations derived from four types of crosses (Susceptible X Resistant, Resistant X Susceptible, Susceptible X Susceptible, and Resistant X Resistant). The genetic analysis indicated that F2 resistance segregation was controlled by three pairs of recessive genes. Among the resistant accessions analyzed, two local landraces Yingxiangxiaoheidou

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and Huipizhihedou were detailed analyzed in China. The inheritance of Yingxiangxiaoheidou which was resistant to race 1, 3 and 4 was analyzed using the F2, F3 population of ZDD2226 (Yingxiangxiaoheidou) x Jindou 23. The results showed that the highly resistance to race 4 of SCN in ZDD2226 was controlled by 1 or 2 couples of recessive allele in F2 population (Wang et al., 2001). The resistance of Yingxianxiaoheidou to SCN race 1 was controlled by recessive genes and the same resistant genes were also in Peking by the populations of BC1F2 lines in crosses of PI88788 × Yingxianxiaoheidou and Peking × Yingxianxiaoheidou. There was 1 pair of gene difference between PI88788 and Yingxianxiaoheidou (Wang et al., 2004), indicating the different genetic mechanisms among various SCN races and crosses. For localize resistance genes associated with race 4 of SCN, an F2 population derived from Yingxiangxiaoheidou (ZDD2226) X Jindou 23 were screened by SSR and ISSR molecular markers with the method of Bulked Segregant Analysis. Two molecular markers, Satt308 and ISSR811 were discovered associated with resistance in Yingxiangxiaoheidou to race 4. Race 1 and race 4 are predominant races of soybean cyst nematode (SCN) in Huang-Huai Valleys in China. The soybean landrace Huipizhiheidou, was recognized as an elite resistance source with mutiple races of race 1, 2, 3, 4 and 5 for mapping QTLs conferring resistance to race 1 and race 4. In 2003, a population of 253 F2:3 families which was generated from a cross between Huipizhiheidou and Jindou 23 were used for molecular marker identification. Three QTLs were mapped to soybean linkage A and G, respectively, conferring resistance to SCN race 4. Of them, one QTL mapped on lingkage group G and is 2.0 cM away from Satt610 marker, named rhg-R4g1 and explains 15.87% of the phenotypic variation. Two QTLs mapped on LG A, one (rhg-R4a1) is 0.2 cM away from Sat_162 and another is 1.6cM (rhg-R4a2) from BSC marker. They could explain 11.31% and 6.15% of the total phenotypic variation respectively (Meng et al., 2003). In 2006, a mapping population with 114 BC1F1 plants of the backcross (Essex×Huipizhiheidou) ×Huipizhiheidou was established and mapped 3 QTLs conferring resistance to SCN race 1 and 5 QTLs conferring resistance to SCN race 4. Of the QTLs related to race 1, 2 (rhgR1-1 and rhgR1-2) located on linkage group G and 1 (rhgR1-3) on linkage group D2. They explained 22.4 % , 21.8 % and 6.2 % of the total phenotypic variation respectively, while rhgR1-1 and rhgR1-3 cosegregated with Sat- 210 and Satt672 , respectively. Of the QTLs related to race 4, rhgR4-1 (within Satt275-Sat-210) and rhgR4-2 (within Sat-168-Sat–141) were located on linkage group G, rhgR4-3 (within Satt442-Sat-401) and rhgR4-4 (within Sat-334-Satt181) were located on linkage group H, and rhgR4-5 within Satt652-Sat-301 was located on linkage group L. They explained 22.8%, 28.9%, 12.0%, 10.5% and 5.9%of the total phenotypic variation respectively, while rhgR4-2 and rhgR4-5 co-segregated with Sat-168 and Satt652, respectively. Suggested that different QTL systems conferred resistance to race 1 and race 4 in Huipizhiheidou (Lu et al., 2006). 4.7.2 Association mapping In order to discover molecular markers related to SCN resistance, 130 SSR markers were used to analyze 41 elite soybean lines from the crosses of Hartwig × Jin 1261, Hartwig × Jin 1265 and Hartwig × Jin 1267 (Yuan et al., 2008) since Hartwig and Jin 1261, Jin 1265 or Jin 1267 had different resistance genes to SCN 4, with genetic similarity of 0.362. Using association mapping method, twenty-two SSR markers from 11 linkage groups were identified as associated with resistance to SCN 4. It has been found that there was at least a

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new resistance gene in Jin strains on linkage group D1b. Moreover, these resistant lines to SCN 4 was effectively identified using two SSR markers of Satt684 and Sat_400. Except above inbred lines, natural population were also used for association mapping in identifyting resistance markers. LD analysis in a natural population consisted of 70 resistant and susceptible soybean accession showed that the average values of D’ and r2 for pairwise comparisons of six SNP marker loci from rhg1 were 0.815 and 0.403, respectively. Most (60%) of the pairwise loci were in significant linkage disequilibrium (P < 0.0001) and the average value of D’ and r2 for the nine significant pairwise loci were 0.869 and 0.587, respectively. The pair 689C>A and 757C>T, at close distance of each other and adjacent to the LRR domain (556-676), had the strongest linkage disequilibrium with the highest D’ and r2 values. No recombination event was detected between them. Pairs of alleles involving the 2868T>C locus and the other loci were not significantly in LD, as was the case for loci 2233G>A and 3995A>C. The minimum number of recombination events separating these groups was 4 (Rm = 4) detected between 757C>T and 2233G>A, 2233G>A and 2564G>A, 2564G>A and 2868T>C, and 2868T>C and 3995A>C (Li et al, 2008). The frequencies of 689C, 757C, 2564G and 3995A in 23 SCN resistant genotypes were 1.00, 1.00, 1.00 and 0.96, respectively. SNP 2564G was also observed in the unique haplotype of susceptible genotype Lee, but the other four were absent in any of the eight genotypes comprising the susceptible population. Therefore, 689C, 757C, 2564G and 3995A can be considered as resistanceassociated SNPs. The haplotype of Heidou 2, resistant to race 2, was identical to the others in the resistant group at the first three SNPs, but had SNP 3995C. The distribution of haplotype 689C-757C in the 23 resistant and 8 susceptible groups was compared with the distribution of alleles present at the Satt309 where a total of five alleles with estimated length of 125bp, 128bp, 131bp, 134bp and 149bp have been detected. In previous studies (Cregan et al. 1999; Wang et al. 2003), alleles 128 bp and 134 bp were considered as markers for ‘resistance’ alleles, and alleles 131 bp and allele 149 bp as markers for ‘susceptibility’ alleles. In the present study, alleles 128 bp and 134 bp coincided with the 689C-757C haplotype, and alleles 131 bp and 149 bp coincided with the 689A-757T haplotype. The 125 bp allele occurred in single genotypes of resistant as well as susceptible genotypes in previous studies, but our results show that the SNPs on 689C>A and 757C>T can successfully separate the resistant (for example, PI88788) from the susceptible genotypes (for example, Lee). Therefore, our haplotypes may be more efficient than Satt309 in markerassisted selection (MAS) (Li et al., 2009). 4.8 Cultivar development resistance to SCN in China Since 1978, the new resistant accessions with yellow seed coat and larger seeds (over 15 grams of 100 seeds weight) had been incorporated into breeding program by crossing and back crossing (Liu et al., 1987). Until now, more than 100 SCN resistant cultivars or lines have been developed in China. In Shandong province, using Beijingxiaoheidou and Haerbinxiaoheidou as the resistant parents, a series of soybean cultivars or lines resistant to SCN such as Qihuang 25, 28 and 29, Qichadou 1, and 2, Gaozuoxuan1hao, Yuejin8hao, 91724, 93746, 93747, and 93748 were bred by artificial hybridization to introduce resistant genotype (Hao et al., 2000). In Beijing, Zhonghuang8, 9, 11, 12, 13 and 17, Zhonghuang57 and Kefeng were developed. By integrating resistant plant introductions with Chinese resistant lines, some new lines and cultivars were developed that had multiple race resistance. For example, 41 elite soybean lines were developed from the crosses of Hartwig×Jin1261, Hartwig×Jin1265 and Hartwig×Jin1267. The index of parasitism (IP) of

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these lines differ largely with a range from 0 to 83.2, even the IP of their all of parent were lower than 3. Among them, 31 lines from different crosses were identified as resistance. Among them, 7 yellow seedcoat cultivars were selected because they posses high yield and high resistance or immunity to race 1 and 4 of SCN (Liu et al. 2008). Jin1263 and 1268 were also develop in Shanxi province except Jin1261, Jin1265 and Jin1267. In Heilongjiang province, the SCN resistant materials were crossed with the local varieties to select the resistant line, such as Kangxian 1, 2, 3, 4 ,5 and 6, Qingfeng1hao, Nenfeng15, Dongnong43, Kenfeng1hao, Ken86-6331, Qingkang83219, Qingkang8408-2, 84-783, 84-793, 84-919 etc. In Jilin province, Jinlin23, 32 and 37, Bainong5, 8 and 9hao were developed (Wang et al., 2002; Wu et al., 1989; Xu et al., 2003). Although a set of soybean cultivars and lines resistant to SCN were bred, they still can not meet the demands of soybean production. Among over 1500 modern cultivars have been developed in China, less 10% of the cultivars have the characteristics of resistance to SCN. Indicated that it is more difficult to develop SCN resistant lines than other elite lines elite lines (Yu and Wang, 2004). By screened 193 cultivars from the soybean regional test in 20042007 for the resistance to race 1 of SCN, Wang et al. (2009) found only 25 cultivars with moderate resistance and no highly resistant cultivar at all. Therefore, it is urgent to enhance the new resistant germplam for breeding new resistant cultivars for sustainable soybean production.

5. Challenging The advances of resistance identification, QTLs or genes discovery, resistant cultivar development in China are not only benefiting the soybean production in China but the worldwide also. A lots of QTLs has been found, but few of them has been cloned and utilized for MAS selection of SCN resistant cultivars. There are several difficulties, including the more genes involved in the interaction between soybean and cyst nematode, minor effect of each genes as well as SCN race population shifting. Moreover, SCN resistant germplasm enhancement also face the challenge because most of soybean resistant accessions identified were dark seed coat, smaller seed size, and vinyl for growth habits. These unfavorable characteristics also need to be overcome for developing resistant cultivars in soybean. Therefore, we should strengthen related basic research, such as cloning resistance genes at genome wide level based on integrating resequencing data, expression files, proteomics data as well as phenomics dada. Released genome sequences for both G. max (Schmutz et al., 2010) and G. soja (unpublised, personal communication) and resequenced one resistant accession (Huipizhiheidou) (unpublised) provide more information for clarifying the genetic mechanism of SCN resistance and discover gene controlling resistance using methodologies emerged recently, such as single feature polymorphism analysis, Illumina Goldengate SNP genotyping, and de novo SNP discovery via RNA-Seq analysis of next-generation sequence data. In addition, expression pattern analysis and function variation of new genes identified using transgenic technique will provides insight into soybean cyst nematode parasitism gene functions and interactions with host soybean. In addition to the analysis methodologies, developing mutants and the near SCN isogenic lines might be the efficient ways for gene discovery for avoiding genetic background influence. On the other hand, we should explore new breeding methods to efficiently utilize the resistant accessions and accelerate the advancement of breeding for soybean production.

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6. Acknowledgments This research was supported by the State High-tech (863) (No.2006AA10A110, 2006AA10Z164, 2006AA10A111), the National Natural Science Foundation of China (No.30490250 and 30471096), State Key Basic Research and Development Plan of China (973) (2010CB125903), and International Science and Technology Cooperation and Exchanges Projects (No. 20061773).

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Liu, Y.& Liu, W.(1989).Identification of races in soybean cyst nematode (second report). Journal of Shenyang Agricultural University, 20(1): 41-44, 1008-9713 Liu, Z.; Lu, W.; Chang, R. & Qiu,L. (2008). Creation of new soybean SCN4 Resistant Lines. Soybean Science, 27(6): 911-914,1000-9841 Li,Y.; Zhang,C.; Gao,Z.; Marinus,J.; Ma,Z.; Liu,Z.; Nan,H.; Chang,R. & Qiu,L. (2009). Development of SNP markers and haplotype analysis of the candidate gene for rhg1, which confers resistance to soybean cyst nematode in soybean. Molecular Breeding, 24: 63–76, 1380-3743 Lu, W.; Gai, J. & Li, W. (2006). Sampling survey and identification of races of soybean cyst nematode (Heterodera glycines Ichinohe) in Huang-Huai valleys. Scientia Agricultura Sinica, 39(2): 306-312, 0578-1752 Lu, P.& Fang, J. (2003). Express analysis of cDNA induced by infecting roots of soybean cyst nematode race 4 in soybean ZZD2315. Molecular Plant Breeding 1(2 ): 193-200, 1672416X Ma ,Y.; Wang ,W.; Liu ,X.; Ma ,F.; Wang ,P.; Chang, R.; & Qiu, L. (2006). Characteristics of soybean genetic diversity and establishment of applied core collection for Chinese soybean cyst nematode resistance. Journal of Intergrative Biology, 48(6): 722-731, 1744-7909 Ma, S.; Zhang, Y.; Xue, Q.; Wu, H.; Liu, H.; Shang, S.; Huo, H.; Wang, F.& Gao, G. (1991). Evaluation of resistance of soybean germplasm to race 3 of soybean cyst nematode. Soybean Science, 10(3): 165-171, 1000-9841 Ma, S.; Zhang, Y.; Xue, Q.; Yao,Y.; Liu,A.; Huang,C.; Li,Y.; Li,Z.; Wang,F.; Gao,T.; Tian,Z. & You,Q. (1996). Evaluation of resistance of soybean germplasms of China to race 3 soybean cyst nematode. Soybean Science, 15(2): 97-102, 1000-9841 Matson, A. & Williams, L. (1965). Evidence of a fourth gene for resistance to the soybean cyst nematode. Crop Science, 5: 477, 0011-183X Meksem, K.; Jamai, A. & Ruben, E. (2005) QTLs for resistance to soybean cyst nematode: an Xa21 like gene family that requires possible dimerization for signal transduction. In: Plant and animal genomes XIII conference. Available via DIALOG. http://www.intl-pag.org/13/abstracts/PAG13_W310.html. Accessed 15–19 Jan 2005 Melito, S.; Heuberger, A.; Cook, D.; Diers, B.; MacGuidwin, A.; & Bent, A. (2010). A nematode demographics assay in transgenic roots reveals no significant impacts of the Rhg1 locus LRR-Kinase on soybean cyst nematode resistance. BMC Plant Biology, 10: 104, 1471-2229 Meng, X.; Liu, XY. & Fang, XJ. (2003). QTL mapping genes conferring nematode in Soybean ZDD2315 public molecular genetic linkage resistance to race 4 of soybean cyst (Glycine max (L) Merr.) based on map. Molecular Plant Breeding, 1(1): 6-21, 1672-416X Nan,H.; Li,Y.; Chang,R. & Qiu,L.(2009). Development and identification of InDel markers based on rhg1 gene for resistanceto soybean cyst nematode (Heterodera glycines Ichinohe). ACTA Agronomica Sinica, 35(7): 1236−1243, 0496-3490 Niblack, T.; Arelli ,P.; Noel, G.; Opperman, C.; Orf, J.; Schmitt, D.; Shannon, J. & Tylka,G. (2002). A revised classification scheme for genetically diverse populations of Heterodera glycines. Journal of Nematology, 34.,279-288, 0022-300X

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Qiu,L.; Li,Y.; Guan,R.; Liu,Z.; Wang,L. &Chang,R. (2009). Establishment representative testing and research progress of soybean core collection and mini core collection. ACTA Agronomica Sinica, 35(4): 571-579, 0496-3490, Qiu, L.; Chang, R.; Wang, W.; Perry, C.; Dechun, W.; Chen, Y. & Ma, F. (2003). Single nucleotide polymorphism ( SNPs) at both loci of rhg1 and Rhg4 in soybean resistant germplasm. Journal of Plant Genetic Resources, 4 (2) :89-93, 1672-1810 Rao-Arelli, A. (1994) Inheritance of resistance to Heterodera glycines race 3 in soybean accessions. Plant Disease, 78: 898-900, 0191-2917 Riggs, R. & Schmitt, D. (1988). Complete characterization of the race scheme for Heterodera glycines. Journal of Nematology, 20(3): 392-395, 0022-300X Ross, J. (1962). Physiological strains of Heterodera glycines. Plant disease reporter, 46: 766-769. Ruben, E.; Jamai, A.; Afzal, J.; Njiti, VN.; Triwitayakorn, K.; Iqbal, MJ.; Yaegashi, S.; Bashir, R.; Kazi, S.; Arelli, P.; Town, CD.; Ishihara, H.; Meksem, K. & Lightfoot, DA. (2006) Genomic analysis of the rhg1 locus: candidate genes that underlie soybean resistance to the cyst nematode. Mol Genet Genomics 276:503–516, 1617-4623. Shang, S. & Liu, H. (1989) The distribution of soybean cyst nematod races in the Northeast region in China. Soybean Science, 8 (4), 382, 1000-9841 Schmutz, J.; Cannon, S.; Schlueter, J.; Ma, J.; Mitros, T.; Nelson, W.; Hyten, D.; Song, Q.; Thelen, J.; Cheng, J.; Cheng, J.; Xu, D.; Hellsten, U.; May, G.; Yu, Y.; Sakurai, T.; Umezawa, T.; Bhattacharyya, M.; Sandhu, D.; Valliyodan, B.; Lindquist, E.; Peto, M.; Grant, D.; Shu, S.; Goodstein, D.; Barry, K.; Futrell-Griggs, M.; Abernathy, B.; Du, J.; Tian, Z.; Zhu, L.; Gill, N.; Joshi, T.; Libault, M.; Sethuraman, A.; Zhang, X.; Shinozaki, K.; Nguyen, H.; Wing, R.; Cregan, P.; Specht, J.; Grimwood, J.; Rokhsar, D.; Stacey, G.; Shoemaker, R. & Jackson, S. (2010). Genome sequence of the palaeopolyploid soybean. Nature, 463: 178-183, 0028-0836 Tian, Z.; Gao, G.; Zhou, C.; Du, Z.; Wu, Y.; Wang, M.; Yang, L. & Li, J. (2007). Study on the variation of soybean cyst nematode. Soybean Science, 26(2): 290-292, 1000-9841 Wang, D.; Lu, W.; Ma, Y.; Liu, N.; Chen, S.; Zheng, J.; Yang, Z.; Liu, R. & Zhi, H. (2009). Evaluation of resistance of soybean cultivars to soybean mosaic virus and soybean cyst nematode. Soybean Science, 28(6):949-953, 1000-9841 Wang, H.; Yu, B.; Duan, Y. &Chen , L. (2007). A sensitive molecular marker SSR associated with resistant gene to heterodera Glycines. Soybean Science, 26(2): 204-212, 1000-9841 Wang, J.; Cheng, D.; Wan, Y. & Fang, X. (2001). Genetic analyzing of resistance to race 4 of soybean cyst nematode in soybean ZDD2226. Chinese Agricultural Science Bulletin,17(6): 12-15, 1000-6850 Wang, L.; Zhao, R.; Wang, L.; Yan, S.; Chen, P. &Li, Q. (2002). Study on soybean breeding for resistance to cyst nematode. Scientia Agricultura Sinica, 35 (5) : 476–481,0578-1752 Wang, S. (2004). Creation of new soybean SCN3-resistant varieties. Journal of Plant Genetic Resources, 5 (1) :62-64,1672-1810 Wang, S.; Li, W.; Liu, S.; Lu, W.; Liang, H.; Wang, T.; Zhang, H.; Zhao, Z. & Sun, W. (2004). Genetic analysis of resistance to soybean cyst nematode (SCN) race 1 in soybean germplasm Yingxianxiaoheidou. Journal of Henan Agricultural Sciences, 5: 29-31, 1004-3268 Wang, W.; Qiu, L.; Chang, R.; Ma, F.; Xie, H. & Lin, F. (2003). Characteristics of alleles at satt309 locus associated with rhg1 gene resistant to scn of Chinese soybean germplasm. Soybean Science, 22 (4 ): 246-250,1000-9841

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Wang ,Z.; Li, Y.; Mu, Z.; Tian, Q. & Shi, Y.(2005). Evaluation of soybean germplasm for resistance to race 4 of the soybean cyst nematode, Soybean Bulletin, 1: 11-12, 10092765 Wu, HL.; Shang, SG. & Liu, HQ. (1989). The resistance to SCN of Long84-783 etc soybean new line, Crops,1: 9, 1001-7283 Wu, HL.; Yao, ZC.; Li, XL.; Liu, HQ. & Shang, SG. (1982) Studies on the screening of resistant sources to cyst nematode of soybean. Scientia Agricultura Sinica, 6:19-24, 0578-1752 Wu, T.; Yang, Q. & Zhao, S.(1992).Identification method for soybean cyst nematode and analysis of inheritance of resistance of race 3.Journal of Northeast Agricultural College,23(4):322-327, 0253-228X Xu, G. (1992). Evaluation of soybean germplasm for resistance to race 3 of the soybean cyst nematode, China Seeds,1: 31-32,1000-6435 Xu, R.; Hao, X.; Wang, C.; Jiang, H.; Li, J. & Shi, C. (2003). Selection of an elite summer soybean line Lu99-2. Journal of Plant Genetic Resources,4 (3): 228-231, 1672-1810 Xu, W.; Sheng,H.; Miao, X. & Yao, W.(2007). Indentification of soybean germplasm resistant to soybean cyst nematode. Soybean Science, 26(3): 377-380, 1000-9841 Yang, L.; Shi, C.; Tian, Z.; Zhou, C.; Li, J.; Wu, Y.&Du, Z.(2010).Mapping QTL associated with resistance to soybean cyst nematode race 3 in cultivar Kangxian 2.Soybean Science ,29(2):215-217, 1000-9841 Yan, Q.; Chen, P. & Wang, L. (1995). The verification of race 4 of soybean cyst nematode in suburban Beijing. Soybean Science, 14(4): 355-359, 1000-9841 Yu, B.; Wang, J. &Ma, S. (2001). Resistance evaluation of major soybean varieties to race 3 of soybean cyst nematode in Heilongjiang province.Journal of Shenyang Agricultural University, 32(3): 181-182, 1008-9713 Yu, B. & Wang, J. (2004). Evaluation of New Soybean Lines of different regions for resistance to race 3 of soybean cyst nematode. Journal of Laiyang Agricultural College, 21 (2): 115-117, 1001-3717 Yu, B.; Duan, Y.; Wang, J.; Li, J. & Chen, L. (2009). Selection of virulent phenotypes from an original race 3 population of heterodera glycines by resistant cultivars. Soybean Science, 28(3): 491-494, 1000-9841 Yuan, C. (2007). Development and application of SNP markers of candidate resistance gene Rhg4 and GmHs1pro-1 and discovery of new resistance gene for soybean cyst nematode in soybean. Ph.D Dissertation. Chinese Academy of Agricultural Sciences Yuan, C.; Li, Y.; Liu , Z.; Guan, R.; Chang, R. & Qiu, L.(2007). A method of SNP genotyping in soybean. Soybean Science, 26(4): 447-459, 1000-9841 Yuan, C.; Lu, W.; Liu , Z.; Li, Y.; Li, W.; Guan, R.;Chang, R.& Qiu, L.(2008). SSR analysis of new developed soybean lines resistant to soybean cystnematode (Heterodera Glycines Ichinohe) race 4. ACTA Agronomica Sinica , 34(10): 1858-1864, 0496-3490 Yuan, C.; Zhou,G .;Li, Y.;Wang,K.;Wang,Z.;Li,X.;Chang,R .&Qiu,L.(2008).Cloning and sequence diversity analysis of GmHs1pro-1in Chinese domesticated and wild soybeans. Molecular Breeding, 22: 593–602, 1380-3743 Zhang, L. (1988). The races of SCN discovered in the north region of Anhui province. Soybean Science, 7(3): 251-254, 1000-9841

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18 Cell-Specific Studies of Soybean Resistance to Its Major Pathogen, the Soybean Cyst Nematode as Revealed by Laser Capture Microdissection, Gene Pathway Analyses and Functional Studies Vincent P. Klink1, Prachi D. Matsye1 and Gary W. Lawrence2 2Department

1Department of Biological Sciences of Biochemistry, Molecular Biology, Entomology and Plant Pathology Mississippi State University Mississippi State, MS 39762 USA

1. Introduction The soybean cyst nematode, Heterodera glycines, is the major pathogen of soybean Soybeans are hosts to a number of pests and pathogens. Of those numerous pathogens, the parasitic nematodes represent the major, ubiquitous, dominant and persistent problem for its cultivation, worldwide. Remarkably, soybeans are hosts to over 100 species of nematodes (reviewed in Sinclair and Backman, 1989). However, the most devastating pathogen of soybean is the soybean cyst nematode, Heterodera glycines. Heterodera glycines were first observed on soybean in Japan in 1881 (reviewed in Schmitt and Noel, 1984). The first scientific description of H. glycines was published in 1952 (Ichinohe, 1952). Up until this time there were no records of H. glycines in the New World. The combination of the growing agronomic status of soybean, the lack of agricultural practices to prevent the spread of any parasitic nematode and the biology of H. glycines set the stage for their rapid dispersal. Heterodera glycines were first identified in the U.S. in 1954 in North Carolina (Winstead et al. 1955). By 1957, H. glycines had been identified as far west as the state of Mississippi (reviewed in Riggs, 2004). Now H. glycines are nearly ubiquitous, found worldwide in 93.5% of the total acreage where soybean is cultivated (reviewed in Riggs 2004). In the U.S., reports rank H. glycines infection as causing more agronomic loss of soybean production than the rest of its pathogens combined (Wrather et al. 2006). H. glycines infection, itself, causes more damage to soybeans in the U.S. (~1.5 billion $) than the entire value of some agricultural crops. These facts demonstrate the significant drain that H. glycines have on soybean production.

2. Life strategies of H. glycines augment its spread The identification of H. glycines throughout regions cultivating soybeans, even in the presence of crop rotation practices, suggests that it can either survive for long periods of

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time in the absence of its host or could live on alternate hosts. Both cases are true. Heterodera glycines can survive in the soil as hardened, desiccated cysts for up to nine years (Inagaki and Tsutsumi, 1971). Other research has demonstrated that H. glycines can reproduce on at least 97 legume and 63 non-legume hosts (Epps and Chambers, 1958; Riggs and Hamblen, 1962, 1966a, b; reviewed in Sinclair and Backmon 1989; reviewed in Riggs 1992). New hosts are identified on a regular basis (Creech and Johnson, 2006). Thus, even though its name implies a tight relationship with soybean as a specialist nematode, its mode of existence more closely relates to that of a polyphagous or generalist pathogen because it can successfully reproduce on multiple species and plant families. This additional facet of the H. glycines life cycle poses a significant problem for control because H. glycines can survive and reproduce on weeds in and around fields throughout the year. This characteristic of their biology is not a minor issue because as high as 93% of fields infected with H. glycines in some locales have such alternate winter annual weed hosts with H. glycines growing at high densities (Creech and Johnson, 2006). Also, H. glycines will infect and detrimentally affect yield in other crops without even completing their life cycle. However, Riggs (1992) noted that many resistant plant species actually prevent penetration altogether. This observation has important ramifications for the development of resistance because it demonstrates that infection is not a default mechanism of the nematode in the presence of any root.

3. The life cycle of H. glycines Heterodera glycines are an obligate endoparasite of soybean with a life cycle of approximately 30 days, depending on temperature and other factors. The timing of the different juvenile stages and associated events (Figure 1) is well established (Lauritis et al. 1983; Koenning, 2004). Prior to hatching, eggs are contained within a cyst. The cyst is a hardened structure composed of the carcass of the female that encases the egg mass. After hatching, the second stage pre-infective juveniles (pi-J2s) emerge from the cyst and migrate toward and burrow into the root. The infective J2s (i-J2s) (Noel 2004) then burrows through the cortex intracellularly toward the root stele by using a tubular mouthpiece called a stylet to slice through the cells. When H. glycines reaches a targeted cell for nurse cell formation (typically a pericycle cell or neighboring root cell), the stylet is used to penetrate the targeted cell. At this point, the nematodes are parasitic J2s (p-J2). The p-J2 then presumably injects substances into the plant cell. These substances are synthesized in esophageal and/or subventral gland cells with each gland cell providing certain substances at specific times during parasitism. The substances initiate the major physiological changes that are observed in the root cell. Shortly after, the infected root cell fuses with neighboring cells. The process begins by breakdown of cell wall material at or near plasmodesmata. The wall openings increase in size, permitting the free flow of cytoplasm, organelles and even nuclei in and out of former cellular boundaries. The repeated cell fusion events produce a syncytium, defined as a multinucleate cell. The mature syncytium, acting as a nurse cell, contains approximately 200 cells sharing a common cytoplasm (Jones and Northcote 1972; Jones, 1981). The p-J2 nematodes that are destined to develop into males then feed for several days. During feeding, the males become sedentary. The feeding process proceeds until the end of their J3 stage. The males then stop feeding and subsequently molt within the former J2 and J3 cuticles into vermiform J4 males. Of note, the J4 males remain encased within the J2 and J3 cuticles until they burrow out of the cuticles and root in preparation for mating. In contrast to the males, the p-J2s that eventually will develop into females become and remain

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sedentary during and after the establishment of their nurse cell. During feeding, the female nematodes increase in size. The process is followed by J3 and J4 molts. During growth, the posterior of the female will erupt out of the root boundary, beyond the root epidermis. This feature of female development provides access to the male that is present outside the root boundary for copulation. After copulation, the adult females will continue to grow while the eggs develop internally. As the life cycle ends for the female its color changes from a creamy white to yellow-tan. This is a visual sign of mortality of the female. However, the eggs within her carcass remain viable. The dead female carcass that encases the eggs, including the eggs, is known as the cyst. The cyst can remain dormant in the field, acting as a repository for viable eggs, for up to 9 years (Inagaki and Tsutsumi, 1971).

Fig. 1. The life cycle of H. glycines during a susceptible and resistant reaction of soybean. Fig. 1A, cysts with eggs (white) hatch. Fig. 1B, second stage pre-infective juveniles (pi-J2) (gray) hatch and migrate toward the root. SUSCEPTIBLE REACTION: Fig. 1Cs, The infective-J2 (iJ2) nematodes (dark gray) burrow into the root and migrate toward the stele. Fig. 1Ds, feeding site selection by the parasitic J2 (p-J2). Fig 1Es, p-J2 nematodes molt into J3, subsequently, they undergo a molt into J4. During this time, the original feeding site is incorporating adjacent cells via cell wall degradation and fusion events. Meanwhile, the male discontinues feeding at the end of its J3 stage. Fig. 1Fs, After maturation, the male and female nematodes copulate. Fig. 1Gs, After ~30 days, the female is clearly visible externally. RESISTANT REACTION: Fig. 1CR, Like the susceptible reaction, the infective-J2 (i-J2) nematodes (dark gray) burrow into the root and migrate toward the root stele. Fig. 1DR, feeding site selection by the parasitic J2 (p-J2). Fig 1ER, the syncytium begins to develop. Fig. 1FR, the syncytium has collapsed resulting in nematode mortality. Figure adapted from Klink et al. (2009c).

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4. Soybean that resist H. glycines infection The United States Department of Agriculture (USDA) collects and maintains thousands of accessions of soybean within the National Plant Germplasm System (http://www.arsgrin.gov/npgs/). The accessions are named according to a plant introduction (PI) scheme. The collections have proven to be a valuable resource because the vast majority of soybean accessions are susceptible to H. glycines (Winstead, 1955). The availability of thousands of soybean accessions (genotypes) makes the identification of resistance in the germplasm possible. For example, resistance of soybean to H. glycines was determined soon after the identification of the nematode in the U.S. (Ross and Brim 1957). The identification of these sources of resistance was initially accomplished by analyzing over 5,800 soybean accessions in two successive large screens (reviewed in Shannon et al. 2004). Most of these sources of resistance play little or no role in commercial breeding programs. The reason is because many of these genotypes carrying resistance genes also carry undesirable traits that can be passed on in classical breeding programs (reviewed in Shannon et al. 2004). Altogether, various screens over the years have identified approximately 118 H. glycines-resistant accessions (Rao-Arelli et al. 1997; reviewed in Shannon et al. 2004). However, the two large screens provided nearly all of the resistance germplasm that is currently bred into commercial varieties. The first screen was performed on 2,800 PIs from this collection. The screen resulted in the identification of the resistant PI 548402 (Peking), PI 84751 PI 209332, PI 90763 and PI 548349 (Ilsoy), each having the undesirable black or brown seed coat (Ross and Brim 1957). From these resistant genotypes, Peking was selected for introgression of resistance germplasm. Thus, Peking is an archetypal source for resistance. Of note, the resistance process of Peking was documented shortly thereafter through histological techniques, revealing the cytology of the resistance reaction to H. glycines (Ross, 1958). This work is described in detail in a subsequent section. The introgression of resistance germplasm was facilitated by genetic crosses with susceptible genotypes having the desirable yellow colored seed coat. The genetic crosses resulted in the development and registration of PI 548988 (Pickett) (Brim and Ross 1966) and PI 548546 (Custer) (Luedders et al. 1968). Unfortunately, field variations in H. glycines were documented by their ability to fully develop on Peking and its progeny lines even before the release of Pickett. The observations prompted a subsequent screen. The second screen of over 3,000 PIs resulted in the identification of additional resistant accessions including PI 88788 (among others) (Reviewed in Shannon et al. 2004). PI 88788 is another archetypal genotype having a different form of resistance that is evident at the cellular level (Kim et al. 1987). The PI 88788 genotype was used to develop and register PI 548974 (Bedford) (Hartwig and Epps, 1978) and PI 518674 (Fayette) (Bernard et al. 1988).

5. Resistance, the cellular processes Plants, composed of numerous cell types, react to and interact with other organisms in a tightly regulated manner whether the outcome is a compatible reaction as in susceptible plants or an incompatible reaction like what happens during a resistant reaction. Thus, the cellular response to the interaction will likely be accompanied by cytological features that characterize the process at each step during its development. The cytology of the resistant reactions in these different accessions has revealed that while commonalities exist in how

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the different soybean genotypes react to H. glycines, features unique to the different accessions are present (Endo, 1965, 1991; Riggs et al. 1973; Acido et al. 1984; Kim et al. 1987; Kim and Riggs 1992; Mahalingam and Skorpska, 1996). Those observations have led to a generic classification scheme of resistance that is based off of those cytological observations (Niblack et al. 2008). The cytological observations along with experimental research demonstrating the effects on H. glycines have proven essential for a molecular dissection of the process at the cellular level (Klink et al. 2007a, 2009a, 2010a, b, 2011). The cytological data of the infection process of H. glycines in soybean roots clearly demonstrate that the syncytium is undergoing two separate phases that lead to a susceptible or resistant reaction (Figure 2), depending on the activity of resistance genes and outcome of any resistance reaction. The two distinct phases during parasitism are an earlier phase referred to as phase 1 and a later phase referred to as phase 2 (Klink et al. 2009b, 2010a, 2011).

Fig. 2. Histology of H. glycines infection at stages during a compatible and incompatible reaction. Peking seedlings were inoculated with incompatible or compatible H. glycines J2 nematodes. Roots were harvested and prepared for histological observation to confirm the establishment of feeding sites at 3 and 6 dpi. Fig. 2A, 3 dpi Peking infected with a compatible nematode, black arrow; area encircled in red, syncytial cell. Fig. 2B, 6 dpi Peking infected with a compatible nematode, black arrowhead; area encircled in red, syncytial cell. Fig. 2C, 3 dpi G. max Peking] infected with an incompatible nematode, black arrowhead; area encircled in red, syncytial cell. Fig. 2D, 6 dpi Peking infected with an incompatible nematode, black arrowhead; area encircled in red, syncytial cell. Bar = 50 μm. Images obtained from Klink et al. 2009a, 2010b.

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During phase 1 the cytological features in the developing nurse cell, that eventually respond by undergoing compatible or incompatible reactions, appear the same (Endo, 1965, 1991; Riggs et al. 1973; Acido et al. 1984; Kim et al. 1987; Kim and Riggs 1992; Klink et al. 2007a, b, 2009a, 2010a, b, 2011). The cellular processes that occur during phase 1 of both compatible and incompatible reactions include the dissolution of cell walls, enlargement of nuclei, hypertrophy, the development of dense cytoplasm and increased ER and ribosome content (Endo, 1965; Riggs et al. 1973; Kim et al. 1987; Mahalingam and Skorpska, 1996). Thus, phase 1 appears the same or nearly so during compatible or incompatible reactions at the cytological level (Figure 2). During phase 2, the cellular response to infection of the compatible and incompatible reactions differs dramatically (Ross, 1958; Endo, 1965, 1991; Riggs et al. 1973; Acido et al. 1984; Kim et al. 1987; Kim and Riggs 1992; Mahalingam and Skorpska, 1996; Klink et al. 2007a, b, 2009a, 2010a, b, 2011) (Figure 2). Phase 2 of the compatible reaction is characterized by hypertrophy of nuclei and nucleoli, proliferation of cytoplasmic organelles, reduction/dissolution of the vacuole and cell expansion as it incorporates adjacent cells (Endo and Veech 1970; Gipson et al 1971; Riggs et al 1973). Eventually these recruited cells merge to form a syncytium that incorporates approximately 200 cells (Jones and Northcote, 1972; Jones, 1981). At this point, it is unclear if different forms of the susceptible reaction exist. In contrast, two major forms of the incompatible reaction have been revealed through screening of the PI collection, followed by cytological examination. All of the different forms of the incompatible reaction can be potent, meaning that almost all nematodes are affected (depending on the population of nematode used in the study). However, the two forms of the incompatible reaction vary in how rapidly they result in the collapse of the syncytium and the timing (juvenile stage) of nematode mortality. Two of the major cytological routes for the incompatible reaction are found in cohorts of soybean accessions that are defined by the archetypal PIs, Peking and PI 88788. A third type of incompatible reaction found in PI 437654 bridges the cytological features of Peking and PI 88788. These forms of the resistant reaction are discussed below. One form of the resistant reaction is found in the archetype Peking (Ross, 1958). The cytology of the Peking-type of reaction is common to Peking, PI 90763, PI 89772 and partially PI 437654. Cytologically, the Peking-type of resistance reaction is evident at the cellular level at 4 dpi. The Peking resistance process involves necrosis of the cells that surround the head of the nematode. This process separates the syncytium from the cells that surround it (Kim et al. 1987). However, syncytia are continuing their later stages of the resistant reaction even at 7 dpi (Riggs et al. 1973). Peking has cell wall appositions. Cell wall appositions are structures defined as physical and chemical barriers to cell penetration (Aist et al. 1976, Schmelzer, 2002; Hardham et al. 2007). Consequently, by 8 dpi the resistant reaction is largely completed. The stage of nematode development that is affected by the resistant reaction has also been determined, occurring at the p-J2 nematode within 4-5 dpi (Endo 1964, 1965; Riggs, 1973; Kim et al, 1987; Kim and Riggs 1992; Colgrove and Niblack, 2008). Molecular analyses of this reaction have been presented (Klink et al. 2007a, 2009a, 2010b, 2011) and will be discussed in a later section. A second form of resistant reaction is found in the archetype PI 88788. In contrast to the Peking-type of resistance, the PI 88788-type of resistance reaction lacks the development of a necrotic layer that surrounds the head of the nematode (Kim et al. 1987). This is an important distinction between the Peking- and PI 88788-types of resistant reactions. The

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initial stages of the PI 88788-type of resistant reaction involves extensive accumulation of cisternae and rough ER and nuclear degeneration within the syncytium by 5 dpi (Kim et al. 1987). There are no thickened cell walls or appositions. Degradation of the cytoplasm is observed by 10 dpi (Kim et al. 1987). The PI 88788-type of resistance reaction results in nematode death at the J3 and J4 stages (Acido et al. 1984; Kim et al. 1987; Colgrove and Niblack, 2008) which is later than that observed for the Peking-type of reaction. Molecular analyses of this reaction have been done (Klink et al. 2010b, 2011) and will be presented in a later section. A third type of resistant reaction occurs in PI 437654. The PI 437654 accession was collected from China much later than those used in the initial screens that determined resistance for Peking and PI 88788. The PI 437654 genotype is a highly underutilized source of resistance. However, new breeding efforts are being done using PI 437654 (Diers et al. 2010). The cytological features of the PI 437654 resistance reaction bridge those of Peking and PI 88788 (Mahalingam and Skorpska, 1996). The PI 437654 resistance reaction becomes evident at both the cytological and ultrastructural levels by 2 dpi, thus, the PI 437654 resistant reaction is the most rapid response. The PI 437654 resistant reaction is characterized by the presence of cell wall appositions, irregular cell wall thickenings, nuclei that degenerate and necrosis of cells (Mahalingam and Skorpska, 1996). Heterodera glycines development is blocked at the p-J2 stage (Mahalingam and Skorpska, 1996; Colgrove and Niblack, 2008). Based on HGtype tests, it was reported that PI 437654 was highly resistant to most populations of H. glycines. Thus, PI 437654 is also the most potent resistant reaction because its activity spans all traditionally accepted H. glycines races. However, H. glycines populations have been identified that will reproduce (albeit at lower levels than a fully susceptible reaction) on PI 437654 (Klink and Lawrence personal observations).

6. Genetics that underlie different forms of the resistant reaction The resistance reactions of Peking, PI 88788 and PI 437654 to H. glycines have been defined through decades of genetic mapping research (Diers et al. 1997; Cregan et al. 1999; reviewed in Concibido et al. 2004; Wu et al. 2009). This information can be highly informative because of the availability of the sequenced soybean genome (Schmutz et al. 2010). Several recessive resistance loci (rhg1 [linkage group G], rhg2 [linkage group M] and rhg3 (Caldwell et al. 1960), and two dominant resistance loci (Rhg4 [linkage group A2]) (Matson and Williams 1965) and (Rhg5 [linkage group J]) (Rao-Arelli 1994) have been identified. Importantly, The Peking, PI 88788 and PI 437654 harbor both common and unique genes (reviewed in Concibido et al. 2004). Peking resistance is explained by four genes. Three of the genes, rhg1, rhg2, and rhg3, are recessive. The three recessive genes are accompanied by the dominant gene Rhg4. Other quantitative trait loci (QTLs) are present. The PI 88788 resistance is explained by four genes. Two of the resistance genes, rhg1 and rhg2 are recessive in nature. In addition to these two recessive genes, PI 88788 resistance involves the dominant genes Rhg4 and Rhg5. Other QTLs are present. The underlying nature of PI 437654 is the recessive genes rhg1, rhg2 and the dominant Rhg4 and Rhg5 genes. A less well understood locus, linkage group I, apparently is the only QTL other than the rhg1, rhg2, Rhg4 and Rhg5 genes found in PI 437654 to be associated with resistance to H. glycines race 14 (Wu et al. 2009). It must be noted that the race 14 designation of H. glycines in Wu et al. (2009) is part of the original and very commonly used classification schemes of Riggs and Schmitt (1988, 1991). However, those races were subsequently redefined as populations because they can only be

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maintained through a sexual cycle (Niblack et al. 2002). As can be imagined, numerous populations can be isolated from a single locale and selected in the greenhouse during a genetic bottlenecking procedure. The procedure that includes numerous generations of single cyst selection and descent results in the generation of numerous populations that behave in unique manners when grown on a panel of soybean genotypes with resistance genes. Thus, the quantity of the H. glycines populations can go well beyond the original 16 races that were identified by Riggs and Schmitt (1988, 1991). Most notably, the HG-type populations do not always easily cross reference back to the original 16 H. glycines races without extensive testing. The observations show that PI 437654, Peking and PI 88788 share rhg1, rhg2, and the Rhg4 genes. In contrast, Peking has the recessive rhg3 while PI 437654 and PI 88788 have the dominant Rhg5. Quantitative trait loci [QTL] mapping have identified other minor QTLs that act in various ways on the different populations of H. glycines. Different combinations of these genes are required for resistance, depending on the soybean genotype and the population of H. glycines involved in the interaction. For details, please refer to Concibido et al. (2004).

7. Genetic variability of H. glycines permits the transcriptomic analysis of both the susceptible and resistant reaction in the identical soybean genetic background Even closely related soybean genotypes can have varying abilities to resist infection by H. glycines (Figure 3). The use of genetic maps and the soybean genome, however, would allow for association mapping of gene expression data directly on to the sequence of the genome (Stolc et al. 2005). This procedure can be done because the map locations of the loci that regulate the resistant reaction of soybean to H. glycines are available. However, additional layers of complexity are added to the analysis by the 16 different known populations of H. glycines and numerous QTLs found in the different soybean genotypes harboring unique resistance genes. For details please refer to (Concibido et al. 2004). There are three major strategies that can be used to perform meaningful cell-based transcriptomic studies of host-pathogen interactions. Two of these very useful methods employ genetic variants of the host to understand compatible and incompatible reactions. The first analyzes near isogenic lines (NILs) and the second analyzes recombinant inbred lines (RILs). Near Isogenic Lines (NILs) are identical at the genetic level except at one or a few genetic loci. Recombinant Inbred Lines (RILs) are formed by crossing two inbred strains followed by repeated selfing or sib mating to create a new inbred line whose genome is a mosaic of the parental genomes. The problem with NIL or RIL-based strategies is that even minor differences in the genome that are present can greatly alter gene expression. For example, even in the absence of any pathogen, different NILs have been shown to possess alterations in gene expression in specific cell types (Hinchliffe et al. 2010). To circumvent this issue, whole genome sequencing can be performed to understand the genetic and transcriptomic nature of the host. Whole genome sequencing is getting to be less expensive and has been done to understand different ecotypes of A. thaliana (Santuari et al. 2010). The analyses are revealing the genetic basis underlying their varying phenotypes (Santuari et al. 2010). Unfortunately, it is typically prohibitive to sequence NILs or RILs, especially for agricultural plants like soybean that have highly duplicated genomes (Schmutz et al. 2010). It is also unclear what differences in genotype are actually responsible for the observed

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Fig. 3. Relative susceptibility of soybean varieties to H. glycines race 15. One hundred twenty five varieties were evaluated for their host response to H. glycines. Only one variety out of 125 examined was considered resistant to this population of the H. glycines. Seeds of each variety were germinated in germination paper for three days. Seedlings were then transplanted singly to pots containing 500cc of sand: soil mix (50:50 v/v). Each soybean variety was inoculated with 2,500 H. glycines eggs recovered from stock plants maintained in the greenhouse. Each variety was replicated 6 times and placed in a randomized complete block design. Tests ran for 50 days prior to harvest. At harvest, the white females and mature cysts are extracted from each pot by hand and then enumerated using a stereomicroscope. Egg and cyst counts are numbers per 500cc soil. The susceptible variety Hutcheson was included in the test to compare the relative susceptibility of each entry to a widely known soybean variety. phenotype. Such a problem was revealed in soybean for the rhg1 locus. The rgh1 locus containing a leucine rich repeat gene believed to be the actual resistance gene for over a decade has been shown to be duplicated with both copies of the duplicated gene having no role in resistance (Melito et al. 2010). However, for example, if the genetic loci that map to the resistance phenotype are known, that region of the genome can be sequenced specifically (Innes et al. 2008). In the case of host-pathogen interactions, the hope is that the alteration in resistance can be attributed to those regions of the genome that are sequenced. The gene(s) are identified in mapped regions experiencing duplicated or lost genes that may or may not be in the sequenced reference genome (Innes et al. 2008). If not, the phenotype may be caused by genes at unmapped loci that lack genetic lesions but experience transcriptomic alterations that account for resistance. In these cases it is possible that the phenotype is caused by alterations in gene expression that are not directly associated with the alterations in genotypes of the NILs or RILs. In this case, alternative approaches need to be used. An alternative approach that provides the purest analyses would be to use the identical plant genotype to accomplish both compatible and incompatible reactions in the presence of resistance genes (i.e. Peking, PI 88788 and PI 437654). In this manner, the activity of the gene itself is used as the marker of the compatible or incompatible reaction. A long history of such analyses exists for studies in soybean and other pathogens (Staskawicz et al. 1984; Bonhoff et al. 1986; Dhawale et al. 1989; Whalen et al. 1991). During the course of those studies it was determined that plant pathogens have specific variations in their genetic

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makeup that underlie their patterns of virulence, influencing how they infect soybean (Staskawicz et al. 1984; Bonhoff et al. 1986; Dhawale et al. 1989; Whalen et al. 1991). Unlike many systems where plant-nematode interactions are studied, related experiments to those already presented are actually possible in soybean because of the availability of different H. glycines field races Golden et al. (1970). The original race designation was based on their ability to reproduce on a panel of four resistant varieties (i.e. Peking, Pickett, PI 88788 and PI 90763). The scheme was later expanded to include sixteen races (Riggs and Schmitt 1988, 1991). Subsequently the races were renamed as populations (Niblack et al. 2002) of H. glycines, based on their sexual life cycle. Currently, there are nine resistant genotypes commonly used in H. glycines type (HG-type) tests that include PI 88788, PI 548402 (Peking), PI 437654, PI 90763, PI 548988 (Pickett), PI 89772, PI 538316 (Cloud), PI 209332 and PI 438489B. Other H. glycines populations have been reported with some of them specifically selected for their virulence patterns on highly resistant soybean genotypes (Niblack et al. 2002). The early studies of Staskawicz et al. (1984) demonstrating virulence factors in other pathogens, established the framework to perform molecular analyses of nematode feeding sites. This framework was strengthened by the demonstration of genetic (Bekal et al. 2008) and transcriptional (Klink et al. 2009b, c) differences in the various H. glycines populations that may underlie their patterns of virulence and ability to infect the different soybean genotypes. Therefore, the available populations, differing in their virulence, can be used to accomplish compatible and incompatible reactions in the same soybean genotype (Mahalingam and Skorpska, 1996; Klink et al. 2007a, 2009c, 2010b), providing the most genetically pure experiments possible for examining the different forms of the resistant reaction.

8. Cell isolation procedures Endo (1971) determined that nucleic acids are synthesized within syncytia. The experiments meant that both DNA and RNA were synthesized and present in these feeding sites. Since compatible and incompatible reactions appear different cytologically for the different forms of the resistant reaction, the variations in the virulence of H. glycines populations can be studied. These observations permit a remarkable series of experiments to be performed that determine how the nematode specifically alters localized gene regulatory mechanisms both spatially and temporally, allowing it to complete its life cycle in an otherwise resistant plant (Klink et al. 2007a, 2009a, 2010b, 2011). The analyses, because they are done in the identical host genotype, can identify the transcriptomic programs that underlie this switch from a genotype’s resistance and susceptibility that is accomplished through the modulation of plant stress responses (Klink et al. 2007a, 2009a, 2010b, 2011). Genomics-based studies of whole roots infected by H. glycines identified different suites of genes that accompanied the susceptible and resistant reactions during the course of infection (Klink et al. 2007b). Those analyses became a model for the cell-type specific studies presented in a later section (Klink et al. 2007a, 2009a, 2010b, 2011) From cytological analyses it is clear that the cellular changes that characterize the incompatible reaction lead to a decrease in nematode growth and ultimately their arrest in development at specific stages. The specificity and consistency of the cytological reactions in the different PIs at the site of infection suggest that while basic aspects of the reaction are likely to be conserved, specific differences are present. This is in agreement with the genetic mapping data (reviewed in Concibido et al. 2004). The obvious problem with the genomics-

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based molecular study of cells embedded within complex tissues is that each cell type, composing the tissue or organ, contributes its own gene expression profile (Birnbaum et al. 2002). Thus, isolating the cells of interest (i.e. the syncytium undergoing compatible or incompatible reactions) that represent a far minority of the cells within the tissue, organ or organism to some level of purity could be done to unravel the intricacies of gene expression that define syncytium biology in each reaction type (Figure 1). After cell isolation, subsequent gene expression studies can be done. As should be imagined, genes pertaining to the resistance reaction may be active or even de-activated specifically in these cells. In contrast, other genes may be influenced by the activities of the nematode, promoting the formation and maintenance of the nurse cell. Whether studying the compatible or incompatible reaction, the genes involved in those processes will be concentrated at the site of infection, the syncytium. Where possible, methods to isolate the cytoplasm from nurse cells have relied on experiments using hand dissections. The first experiments obtained giant cells from galls induced by the root knot nematode (Meloidogyne incognita) during a compatible interaction in tomato (Lycopersicon esculentum) (Wilson et al. 1994). The advantage of experiments using the root knot nematode is that infected root regions are obvious because of the formation of enlarged galls on the roots that can be dissected. The experiments permitted the isolation of RNA from those cells and the construction of cDNA (Wilson et al. 1994). Relatively few of the cDNAs turned out to be gall specific. This outcome would be expected since they were plant genes involved in basic metabolic processes (Bird and Wilson 1994). However, the significance of the experiments was that they demonstrated the efficacy of the approach in isolating RNA from those cell types. Other more recent experiments performed in the model organism A. thaliana have demonstrated that it is possible to dissect out root regions where giant cells were developing during infection by M. incognita (Jammes et al. 2005). The experiments provided a genomics-based blueprint for giant cell development over a wide range of time points (Jammes et al. 2005). The work also confirmed the expression of a panel of genes through promoter-reporter assays (Jammes et al. 2005). In contrast to plants experiencing root knot nematode infection, hand dissections cannot be used to isolate syncytia induced to form by the activities of H. glycines. The reason is because syncytia formation is not accompanied by the development of obvious swollen root regions as happens during infection by root knot nematodes. Thus, the identification of H. glycinesinfected root regions is not an obvious or a straightforward procedure. Also, the root region is not dominated by cytoplasm from the nurse cell as in the case with the root knot nematodes. Therefore, alternative methods would have to be adapted to accomplish the same task for genomics-based analyses in soybean. Such a method had its roots in experiments performed in the 1970s. Laser capture microdissection (LCM) is an alternative means that affords a high degree of precision and accuracy to isolate homogeneous cell populations that are otherwise technically difficult to isolate. The original work that developed the technology was done in research that studied complex animal tissues. The technological achievement involved engineering a microscope with a laser beam to isolate cells from freeze-dried samples (Isenberg et al. 1976; Meier-Ruge et al. 1976). The procedure was largely forgotten about until Emmert-Buck et al. (1996) redesigned the microscope so that it could be used to isolate cells from histological sections for molecular work. Later work used single cell gene profiling (in the absence of LCM) in animal pancreatic samples (Chiang and Melton, 20002)

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showed the importance of single cell profiling techniques in investigating developmental processes. The first plant-based research followed (Asano et al. 2002) showing the efficacy of the LCM approach for molecular analyses. The successes gained by use of LCM in studying H. glycines infection is based on the ability to use H. glycines itself as an in situ physical marker to locate the syncytium in infected tissues prior to LCM (Klink et al. 2005) (Figure 2). The use of LCM in studying cyst-forming nematodes demonstrated how the LCM technology would be a major advancement in investigating plant pathogen interactions at the genomics level (Klink et al. 2005). The LCM procedure allowed for the collection of feeding sites to the exclusion of other cell types not involved in infection (Figure 4). LCM has proven to be especially valuable to study the development of the syncytium during soybean infection by H. glycines in comparative analyses of compatible and incompatible reactions (Klink et al. 2005, 2007b, 2009b, 2010a, b). It has also been extremely useful in studying the various forms of the resistant reaction (Klink et al. 2011). Thus, like in animal studies, LCM would be of value in studying plant developmental and pathological processes (Klink et al. 2005, 2007b, 2009b, 2010a, b; 2011). This capability of LCM would allow for the identification of genes specifically involved in either the resistant or susceptible reaction without other cell types interfering in the analysis procedures.

Fig. 4. Microdissection of a syncytial cell. Fig. 4A, a 3 dpi time point syncytial cell (area encircled in red) prior to microdissection was identified by their proximity to H. glycines (three black arrows). Fig. 4B, the same syncytial cell section from Fig. 4A after microdissection; microdissected syncytial cell (area between white arrows).

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9. Genomics- based analyses of the developing syncytium undergoing a resistant reaction Prior to genomics-based methods, gene expression studies of nurse cells relied heavily on reporter-based assays (reviewed in Gheysen and Fenoll, 2002; de Almeida Engler et al, 2004; Jammes et al. 2005; Klink et al. 2005). Such experiments are important for a variety of reasons including the demonstration of enhanced expression in the nurse cell as compared to the surrounding cells. Unfortunately, the expression of only a few genes could be demonstrated at any one time. In contrast, genomics-based analyses procedures involving mircoarrays (Schena et al. 1995) are a way to study the expression levels of thousands of genes simultaneously. Since the LCM procedure purifies the cell populations to the exclusion of the unassociated cells, the procedure will demonstrate gene expression that is specific to the nurse cell. However, even though the RNA is isolated from the nurse cells, it does not mean that these genes cannot be expressed at higher or lower levels or not at all in other cell types within the plant. Methodologies to analyze gene expression occurring during syncytium development have employed two major procedures; (1) differential expression (DE) and (2) detection call methodology (DCM). Each method is an important type of microarray analysis. The original microarray analysis that studied syncytium gene expression during both compatible and incompatible reactions in soybean employed both methodologies. The differential expression (DE) (Klink et al. 2007a, 2009a, 2010a, 2011) and detection call methodology (DCM) (Klink et al. 2010b) have various advantages because the cells under study are homogeneous populations.

10. Differential Expression (DE) Analyses in studying homogeneous cell populations The more common gene expression analysis procedure is the DE analysis. The premise of DE analyses is that one mRNA sample, such as an uninfected root sample is used as a base line (control) for gene expression experiments. The comparison is made to a second experimental mRNA sample such as one isolated from a nematode infected root. For DE analyses to be possible, the gene must be expressed in each sample type in order for statistical analyses to be possible (Figure 5). If the gene is expressed (or present) in each sample type then differential expression analyses can be performed. Otherwise, if the gene is present on one sample and absent in the other, the probe set is discarded from the analysis. This is because statistics cannot be done on that probe set even though, biologically speaking, it may be differentially expressed to an extent even more so than many genes that make it into DE analyses. If the gene is present in both sample types, one of three properties of gene expression can be determined in comparisons between experimental and control samples. Through statistical analyses, if the gene transcript is present in higher quantities in the experimental sample than the control, the gene is considered up regulated or induced in expression. In contrast, if the gene transcript is lower in quantity that in the experimental than the control sample, the gene is considered down regulated or suppressed in expression. The last alternative is that there is no change in quantity of transcript between the experimental and the control. Thus, its expression is unaltered. While sophisticated statistical analyses are available to provide confidence in the results provided by DE analyses, many genes are excluded from the analysis because, as mentioned

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Fig. 5. Sorting microarray data through differential expression (DE) and detection call methodology (DCM). Pools A and B are different sample types. Analyses have identified genes that are unique to A (purple), genes that are unique to B (green) and genes common to both pools (A U B). Only genes that are present in both A and B can be used for differential expression analyses. The red circle represents the genes that are present in both pools and are also differentially expressed, measuring induced gene activity. The blue circle represents the genes that are present in both pools that are differentially expressed, measuring suppressed gene activity. The remaining genes (yellow) are present in both samples (common) but are not differentially expressed. Image adapted from Klink et al. 2010b. earlier, they are detected in only one of the two sample types. In these cases, especially when different cell types are under study, it is important to investigate why a particular transcript is present in one sample type and absent in another. This is especially important when the genes are expressed only in syncytia undergoing an incompatible reaction. The reason why it is important to go back and investigate these pools of discarded genes is that they may actually be the genes that define the resistant reaction. Those analyses (see below) have been performed, revealing genes expressed specifically within the syncytium undergoing a resistant reaction (Klink et al. 2010b).

11. Detection Call Methodology (DCM) in studying homogeneous cell populations An important facet of microarray methods is that they provide useful information on both the numbers and types of transcripts that are present or absent within samples (Hill et al. 2000) (Figure 5). DCM is an underutilized form of microarray analysis because it identifies genes that are actively expressed within a sample (Seo et al., 2004; McClintick and Edenberg, 2006; Rème et al., 2008; Klink et al. 2010b; Archer and Reese, 2010). The DCM may not necessarily provide information on how actively expressed the gene actually is like that provided by the DE analysis. However, DCM has been used in a variety of studies to simply answer the question of what genes are actively expressed within a particular sample under study (Hill et al. 2000; Birnbaum et al. 2003; Poroyko et al. 2005). DCM-based studies include comparative analysis of syncytium development during a compatible and incompatible reaction (Klink et al. 2010b). DCM may be a good way to identify rare transcripts that are lost in DE analyses (Klink et al. 2010b). The DCM is a statistically sound method. Several recent papers have used DCM to understand transcription in various model experimental systems (Seo et al., 2004; McClintick and Edenberg, 2006; Rème et al., 2008). The utility of the method is that genes that are actively expressed can be detected within a particular sample type. This procedure provides a relatively inexpensive assessment of what genes are actively expressed in a cell

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type. An extension of the DCM is to use them to compare transcripts between different cell types or of the same cell type and at different points during a time course (Hill et al. 2000; Birnbaum et al. 2003; Poroyko et al. 2005). The DCM takes into consideration only the presence of the transcript as measured by the probe set on the microarray. Thus, DCM can be used as a measurement of the diversity of transcripts within those samples during a developmental process (Figure 5). This is a very important aspect of the DCM because complex tissues and organs are composed of numerous cell types, each with its own gene expression pattern (Birnbaum et al. 2003). As it should be imagined, each of the different cell types would have a basic gene expression pattern that could be studied between two samples. This is because many genes would be expected to be expressed within a sample pair (i.e. control and experimental). However, in the case of studying gene expression of individual homogeneous cell populations, many genes (including rare transcripts) would be expected to be uniquely expressed in one of the two sample types, such as those activated during the incompatible reaction in the syncytium (Klink et al. 2010b). Unfortunately, genes experiencing this expression profile, detected only in the sample undergoing the resistant reaction and not the control sample, would be thrown out of the DE analysis. Unless cognizant of DE methodologies, the genes are not considered in downstream analyses. As can be imagined, these discarded genes may actually define the resistance reaction since they are expressed only in cells undergoing that reaction as compared to the control that lack their expression. While DE analyses dominate biological investigations, DCM-based analyses are a well suited analysis tool for investigations such as those involving homogeneous cell populations undergoing a specific process such as a resistant reaction (Klink et al. 2010b).

12. Cellular analyses of the resistant reaction of Peking and PI 88788 Genomics-based analyses of the resistant reaction of the Peking-type of reaction have identified highly induced activities of genes including jasmonic acid signaling that include liopoxygenase (LOX) and JASMONATE RESISTANT 1 (JAR1) (Klink et al. 2007a, 2009a). LOX expression is also highly active in PI 88788 (Klink et al. 2010a). LOX is not only associated with plant development and responses to environmental change, but are known well for their involvement in plant defense to pathogen challenge (Song et al. 1993). LOXs are non-heme dioxygenases, catalyzing the oxygenation of polyunsaturated fatty acids. This activity leads to hydroperoxide generation. Hydroperoxides can be converted into (1) stable aldehydes, (2) hydroxy- and epoxy-fatty acids that exhibit antimicrobial activity and (3) jasmonic acid. Jasmonic acid is derived from the LOX product 13-hydroperoxyoctadecatrienoic acid and has been shown genetically to be involved in plant defense responses (Vijayan et al. 1998; Rancé et al. 1998). Importantly, ZmLOX3 is shown to control resistance to M. incognita (Gao et al. 2008). In agreement with this observation, a homolog of the A. thaliana gene JASMONATE RESISTANT 1 (JAR1) is induced during the incompatible reaction (Klink et al. 2009b). JAR1 is an ATP-dependent JA-amido synthetase (Staswick et al. 2002; Staswick and Tiryaki 2004) that is essential for defense against the soil fungus, Pythium irregulare (Staswick et al. 1998). JAR homologs are present in various plant families. Therefore the genes and their activities are conserved in response to pathogens, having various roles in the defense response (Wang et al. 2007, 2008). Other highly induced genes include S-adenosylmethionine synthetase (SAM) (Klink et al. 2009a). SAM catalyzes the formation of S-adenosyl-L-methionine. This product is an

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important methyl group donor in most transmethylation reactions that participate in plant lipid, nucleic acid, polysaccharide, secondary products and polysaccharide synthesis. Sadenosyl-L-methionine is a precursor of ethylene and is also necessary for constituents of the phenylpropanoid pathway, a pathway involved in lignin biosynthesis. The formation of cell wall appositions in Peking are consistent with the induced activity of genes involved in lignin biosynthesis such as SAM. Cell wall appositions have been known for over a century (Aist, 1976). The structures have been measured and are well known to be involved in plant defense to fungal pathogens (Israel et al. 1980). This is an important observation because of the presence of cell wall appositions during the resistant reaction in Peking and PI 437654 but not in PI 88788. The cell wall appositions contain several substances such as phenolics, callose, lignin, chitin, lipids, suberin, silicon and other cell wall proteins such as hydroxyproline-rich glycoproteins and peroxidases (McLusky et al. 1999). All of these substances or the enzymes involved in their synthesis have been found in genomics analyses of the syncytium undergoing an incompatible reaction in Peking and PI 88788 (Klink et al. 2007a, 2009a, 2010b, 2011). An important facet of this activity is that the metabolic process is accompanied by a local release of reactive oxygen species (ROS) (Levine et al. 1994; Tenhaken et al. 1995; Hueckelhoven et al. 1999). This activity includes the release of hydrogen peroxide (Levine et al. 1994; Tenhaken et al. 1995), consistent with experiments of the resistant reaction in Peking that compared the incompatible syncytia to the compatible syncytia (Klink et al. 2007a). It was noted that the soybean actin homolog, ACT-7, was also induced during the incompatible reaction. Both hormone treatment and wounding induces ACT-7 expression, being required for hormone-induced callus formation in A. thaliana (Hardham et al. 2008), in agreement with cell wall apposition development in Peking (Klink et al. 2007b, 2009b). In agreement with these observations, components of the phenylpropanoid pathway are induced in their activity in syncytia undergoing an incompatible reaction (Klink et al. 2009b). Some of these genes included phenylalanine ammonia lyase (PAL), Cinnamoyl-CoA reductase (CCR) and caffeic acid O-methyltransferase (COMT). PAL is a key enzyme in the phenylpropanoid pathway, catalyzing the first reaction of phenylpropanoid biosynthesis. PAL converts phenylalanine into trans-cinnamic acid, a substance that can be both hydroxylated and methylated. These derivatives can produce compounds such as coumaric, caffeic, and ferulic acids, substances that are toxic to both herbivores and pathogens (Cole 1984; Leszczynski et al. 1989; Verpoorte and Alfermann 2000; Morello et al. 2005; Wang et al. 2006). CCR activity occurs several steps downstream of PAL of the lignin biosynthesis pathway. CCR catalyzes the conversion of cinnamoyl- CoA esters to their respective cinnamaldehydes. Of note, CCR is the first enzyme of the monolignolspecific part of the lignin biosynthetic pathway (Raes et al. 2003; Mir Derikvand et al. 2008). COMT is involved in the later steps of lignin biosynthesis (Goujon et al. 2003; Raes et al. 2003; Ferrer et al. 2008). In A. thaliana, COMT mutants are deficient in lignin biosynthesis and production of soluble phenolics (Goujon et al. 2003). The COMT mutant, atomt1, also affects the expression of genes pertaining to monolignol biosynthesis (Goujon et al. 2003). The induced states of these enzymes (Klink et al. 2007a, 2009a) are consistent with the presence of cell wall appositions in Peking and observations of thickened cell walls in cells undergoing a resistant reaction. The induced states of key enzymes of the lignin biosynthesis are just one pathway of many that exhibit altered states of expression as a consequence of the resistant reaction (Klink et al. 2007a, 2009a, 2011). In addition to the jasmonic acid and

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phenylpropanoid biosynthetic pathways, numerous genes are also specifically suppressed during the interaction between H. glycines and soybean (Klink et al. 2009b). The potent but prolonged resistant reaction of PI 88788 was also studied, demonstrating similarities in gene expression exist between Peking and PI 88788 (Klink et al. 2010a, 2011). The gene expression analyses revealed highly induced activities of components of the jasmonic acid signaling pathway. The analyses also identified induced activities of the phenylpropanoid pathway. However, direct comparisons of the two forms of the resistant reaction were needed to determine what differences existed between Peking and PI 88788.

13. Comparative genomics analyses of the different forms of the resistant reaction As mentioned in an earlier section, two major forms of the resistant reaction exist in soybean, Peking and PI 88788. Between the Peking and PI 88788 forms, the rate at which the potent reaction occurs is more rapid in Peking. Altered H. glycines development is observed in the p-J2 stage in Peking (Endo 1964, 1965; Riggs, 1973; Kim et al, 1987; Kim and Riggs 1992), followed by altered development at the J3 and J4 stages in PI 88788 (Kim et al. 1987). In determining the different forms of the incompatible reaction, it is possible through bioinformatics procedures to make comparative analyses between different genotypes at the cellular level (Klink et al. 2011). Research in other plant–pathogen systems has also demonstrated that this is possible. In interactions between the pathogen Fusarium graminearum (Fusarium head blight) and the wheat genotypes Triticum aestivum L. cv. Ning (resistant) 7840 and T. aestivum L. cv. Clark (susceptible), comparative studies have been performed using whole plant organs. However, as discussed earlier, the studies began by identifying genotype-specific differences in gene expression that are present even prior to pathogen invasion (Bernardo et al. 2007). Variations in gene expression are present between Peking and PI 88788 (Klink et al. 2011). In some cases, these differences in gene expression occur in the absence of a pathogen prior to infection (Klink et al. 2011). The differences in gene expression may be an important basic property of the different genotypes to consider since it is possible that the relative differences in gene activity presages their ability to resist infection by H. glycines. For example, substantially higher levels of the differentially expressed in response to arachidonic acid 1 gene (DEA1) and a protease inhibitor are present in Peking as compared to PI 88788 (Klink et al. 2011). In tomato, DEA1 exhibits organspecific expression, being highly expressed in roots, stems, and leaves (Weyman et al. 2006a). The DEA1 gene is induced by arachidonic acid (AA). This observation could be an important clue to the rapid and potent resistant reaction of Peking. AA is a polyunsaturated fatty acid molecule that is produced by various pathogens (i.e. Phytopthora infestans). The molecule is known to trigger programmed cell death (PCD), a hallmark of the resistant reaction in Peking (Kim and Riggs 1992). In other plant-pathogen interactions, AA is shown to be released from germinating P. infestans spores (Ricker and Bostock, 1992) and can mimic the PCD response (Bostock et al. 1981; Bostock et al. 1986). While there are many gaps remaining in the role of DEA1, it has been shown to have a conserved, shared domain found in the eight-cysteine motif superfamily of protease inhibitors. The domain is also found in proteins such as alpha-amylase inhibitors, lipid transfer proteins and seed storage proteins (Weyman et al. 2006a). Other experiments including reporter analyses involving normal protoplasts and protoplasts undergoing plasmolysis show that DEA1 is associated with the cell membrane (Weyman et al. 2006a). In a heterologous yeast system, DEA1 protects the

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yeast from freezing death (Weyman et al. 2006b). AA, since it functions upstream of jasmonate signaling (Blee, 2002), may provide a way to amplify the signal leading to the rapid and potent resistant reaction of Peking. The possibility that AA is associated with resistance is consistent with knowledge of the involvement of jasmonate signaling in the resistance of plants to parasitic nematodes (Gao et al. 2008). The analyses done on Peking and PI 88788 demonstrated that the plant-pathogen interactions are, requiring better ways to visualize the data. This gap was filled by the development of customized pathway visualization aids that could take the gene expression data and overlay them on visual gene pathway charts.

14. Pathway analyses The genomics-based analyses of the cell-specific interactions occurring between soybean and H. glycines during their respective compatible and incompatible reactions resulted in a substantial amount of data. The data requires useful processing in order to put it into context. Even when annotated, the genes are typically presented in list form. Without extensive knowledge of gene function or computational power it can be difficult to determine the context of the genes or the significance of their activity. Earlier analyses that have studied plant-pathogen interactions in A. thaliana during infection by Pseudomonas syringae pv. Tomato determined the pathways whose expression was altered during infection (Scheideler et al. 2001). An important outcome of those analyses was that the pathway analyses identified a major metabolic shift from gene expression centering on housekeeping to pathogen defense (Scheideler et al. 2001). The far more reaching impact was that the analyses showed how complex genomics-based information can be massaged into a useful form in less genetically tractable agricultural plants such as soybean. For some organisms (i.e. soybean), it is difficult to take large amounts of genomics-based information and cluster the information into useful pathway information. Part of the problem is that, to date, most molecular-based plant research has been generated in the model organism A. thaliana. Data for A. thaliana is housed in the data warehouse found at http://www.arabidopsis.org/. While A. thaliana is the model for molecular and genetic information in plants, the vast majority of molecular research and data warehouse development that has identified gene function has been done in the animal genetic models; Caenorhabditis elegans (http://www.wormbase.org/) and Drosophila melanogaster (http://flybase.org/) with other work done in the budding yeast model Saccharomyces cerivisiae (http://www.yeastgenome.org/). Thus, to make use of the massive amount of functional data as it pertains to soybean, it would be important to have a useful annotation of the soybean expressed sequence tag (EST) annotation. This information has recently been provided for the EST collection (Le et al. 2007) and an annotated reference genome (http://www.phytozome.net/soybean) (Schmutz et al. 2010). Through cross comparative analyses, genes that are conserved in primary DNA or protein structure can be identified. However, most of the work for soybean resides in customized data warehouses that are not really easily accessible to most labs. One of the original ways to visualize gene pathways was the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www.genome.ad.jp/kegg/) (Goto et al., 1996). KEGG was designed to computerize the current knowledge of metabolic and regulatory pathways and is an exceptional platform to obtain a deeper understanding of the genetic

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circuitry within the cells under study. KEGG has been expanded to include a plant database (Masoudi-Nejad et al., 2008) that is useful for a variety of purposes, including useful secondary metabolism maps http://www.genome.ad.jp/kegg/atlas/. To further explore these genes, the Database for Annotation, Visualization and Integrated Discovery (DAVID) can be used as a platform to obtain additional information. DAVID (http://david.abcc.ncifcrf.gov/home.jsp) was erected by the National Institute of Allergy and Infectious Diseases (NIAID) with the mission of providing laboratory and bioinformatics support. To take full advantage of the well-known KEGG pathways, the DAVID pathway viewer can display genes from a user’s list on pathway maps to facilitate biological interpretation in a network context. Once the KEGG annotations are complete, those data can be organized and stored into a relational database. The expression levels of genes can be correlated to a particular biochemical, physiological or metabolic role as it pertains to what developmental process or pathological interaction the cell is undergoing (Figure 6).

Fig. 6. Pathway analyses of genes involved in fatty acid biosynthesis of Peking. Pathway analyses were performed by determining the soybean homologs of genes found in the different pathways available in KEGG. Expression data from both Peking and PI 88788 genotypes were combined. Differential expression data obtained from laser captured syncytia was then used to color code genes found in the KEGG pathways. Legend: Green = induced gene; Red = suppressed gene; yellow = induced but not at a statistically significant level; white = no expression data. The expression map represents the combined Peking + PI 88788 expression data from the 6 dpi time point as compared to the combined Peking + PI 88788 expression data from uninfected pericycle cells (control). Image adapted from Klink et al. 2011.

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15. Caution should be exhibited to pathway-based analyses Bioinformatics-based comparisons of different forms of the resistant reaction to H. glycines at single cell type resolution are revealing commonalities and unique features of the different forms of the resistant reaction in soybean. Customized pathway analyses have presented the activity of specific genes composing various pathways in Peking as compared to PI 88788 (Klink et al. 2011). An example of one of the custom analyses is provided (Figure 6). However, the major problem with pathway analyses is that many genes lack homology to known genes. These genes may be conserved in primary nucleotide sequence or even protein sequence but lack any known function. The genes may lack known protein domains. The genes may have organism-specific functions (i.e. soybean). In these cases, the genes will escape meaningful analyses or placement in a pathway because they have no annotation. Genes of unknown function or annotation pose an important problem in obtaining knowledge on the cellular features of the incompatible reaction. The observations reveal a significant gap in knowledge between the ability to identify gene expression (and hence genes) and the capability to test the function of those genes in important agricultural crops like soybean.

16. Functional genomics research: the development of a rapid cycling, small soybean plant In contrast to their field grown counterparts, dwarf varieties of agricultural plants are an avenue that allows large numbers of plants to be grown in small areas. Sometimes, the dwarf varieties have the added benefit of a rapid life cycle. For example, the dwarf tomato, Lycopersicon esculentum cv. Micro-Tom (Scott and Harbaugh 1989; Meissner et al. 1997) has proven extremely useful in rapidly identifying genes that control tomato development (Meissner et al. 2000; Marti et al. 2006). Some of the studies involve a procedure for rapid and efficient transformation (Sun et al. 2006: Orzaez et al. 2006). The development of the soybean genotype MiniMax (PI 643148), a dwarf, rapid cycling, MG 000 plant that grows as quickly and is as tall as the genetic model A. thaliana (Matthews et al. 2007; Klink et al. 2008) may fill that void (Figure 7). MiniMax is well suited for forward genetic analyses that include the development of NILs and RILs because they can be grown year-round in the greenhouse with little effort (Klink et al. 2008). MiniMax can also be used for mutational analyses. An additional advantage of MiniMax is that the rapid growth habit carries over into the tissue culture process (Klink et al. 2008). This property of MiniMax makes the isolation of transformed somatic embryos much more rapid than standard soybean genotypes, allowing for rapid reverse genetic analyses to be performed.

17. An alternative strategy for developmental genomics-based screens to study root pathogens Soybean presents many technical challenges to studies testing gene function because of how problematic plant transformation is to most labs. These technical problems have been addressed by massive efforts over the years in various labs focusing in on various aspects of soybean transformation (Ranch et al. 1986; Hinchee et al. 1988; Parrott et al. 1989; Santarem et al. 1997; Santarem and Finer 1999; Tomlin et al. 2002; Ko et al. 2004; Collier et al. 2005; Klink et al. 2008, 2009b). A problem encountered during the early stages of the development

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Fig. 7. MiniMax (PI 643148) three weeks after planting with young pods and flowers.

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of a plant transformation pipeline for the study of H. glycines was the features to design into the vectors. At a minimum, it would be important to have vectors in hand that could be used for the overexpression of genes, the knock down gene activity through RNA interference (RNAi) (Fire et al. 1998; Klink and Wolniak 2000, 2001) and promoter analyses. However, the vectors would have to actually function in soybean under a variety of welldefined conditions. An advantage for H. glycines research was that transformed soybean roots could be kept in tissue culture (Savka et al. 1990; Cho et al. 2001). In addition, modifications of those procedures have led to the development of chimeras that had transformed roots and untransformed aerial portions of the plant (Collier et al. 2005; Klink et al. 2008, 2009b). The ability to obtain transformed roots through their interaction with microorganisms had their beginnings in the work of Tepfer (1984). The experiments used the known influence that A. rhizogenes had on root development. The influence is known as hairy roots because of the dense clustering of root development that was caused by the transmission of genes from the root inducing (Ri) plasmid of A. rhizogenes into the genome of the plant cell. The most efficient hairy root transformation procedure in soybean for H. glycines research relies on the bacterium, A. rhizogenes strain K599 (Haas et al. 1995). Initial tissue culture-based analyses using the hairy root system have been employed in studying H. glycines development (Savka et al. 1990; Cho et al. 2001; Li et al. 2010a). The hairy root technology could be used for overexpression, promoter analyses and RNAi studies Limpkins et al. (2004). However, it was possible that overexpression and RNAi-based studies could become negatively influenced if the presence of the nematode in the infected root shut down or suppressed the activity of the promoter driving transgene expression. The figwort mosaic virus sub genomic transcript (FMV-sgt) promoter (Bhattacharyya et al. 2002) allows for high levels of expression of transgenes throughout the course of nematode infection of soybean roots in numerous genotypes (Klink et al. 2008). New Gateway® compatible versions of the original plasmid DNA vectors used in those studies facilitate high throughput functional studies of the genes identified through the gene expression studies (Klink et al. 2009b). A different strategy that obtains whole plants uses the hairy root procedure to obtain transformed roots that develop on the untransformed seeding. The method produces plants known as composite plants due to their chimeric nature (Collier et al. 2005; Klink et al. 2008, 2009b). This procedure was a major advancement for studying root biology (Collier et al. 2005). An important feature of the composite plant technology is that the transformation procedure can be done in the absence of tissue culture or axenic conditions (Collier et al. 2005; Klink et al. 2008, 2009b). Tissue culture is not needed because the plants are selected by the presence of the enhanced green fluorescent protein (GFP) (Haseloff et al. 2007) as a visual marker. Gateway®-compatible vectors (Klink et al. 2009b) designed for their use in soybean make highthroughput analysis a reality. During this transformation procedure in soybean, dozens of roots are produced on any individual plantlet (Klink et al. 2009b). However, only those roots that are transformed as observed by their GFP expression are allowed to grow (Klink et al. 2009b). Roots lacking fluorescence are trimmed off of the stock and replanted (Klink et al. 2009b). Transformation efficiencies are usually around 100% of the stocks exhibiting positive GFP fluorescence. The composite plants are ready within 40-45 days for experiments in soybean (Klink et al. 2009b). Quantitative real time PCR (qRT-PCR) is used to demonstrate the expression of the transgene. Of note, whole transformed plants have been generated from transformed hairy roots (Tepfer 1984; Zdravkovic-Korac et al. 2004; Crane et al. 2006). This observation provides an additional route that can be taken to

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obtain whole transformed plants. The method has been used to obtain soybean roots that are resistant to H. glycines infection by engineering H. glycines genes in as inverted hairpin repeats, resulting in an RNAi phenocopy of gene activity (Klink et al. 2009b; Li et al. 2010b).

18. Summary With new technologies, the resistant reaction of soybean to the H. glycines is being studied at the resolution of its nurse cell known as the syncytium. LCM, in concert with genomics-based analysis methods, is identifying genes that are specific not only to the susceptible or resistant reaction, but also to the different forms of the resistant reaction found in different soybean genotypes (Klink et al. 2005, 2007b, 2009a, 2010a, b, 2011). The application of the technologies and procedures outlined in this review are allowing for the identification of common strategies that the many different soybean genotypes use to combat plant parasitic nematode infection. The method has application even to study other pathogens or symbionts. However, the methods underscore the importance of the development of meaningful gene annotation databases that can maximize the usefulness of the information. Equally important are public availability of the data and that it is easy to mine. The development of the tools will allow many labs access to explore the function of genes in meaningful genomics analyses that are actually functional in nature (Klink et al. 2009a). Databases housing and storing data in model genetic organisms have already demonstrated the value of such databases to the research community. When these resources are developed, real solutions to real agricultural problems, whether local and specialized or a global and generalist scale, such as those presented by plant parasitic nematodes will be achieved.

19. Acknowledgements The authors thank the Department of Biological Sciences at Mississippi State University for support and resources. The authors thank the Mississippi Soybean Promotion Board for support. VPK also thanks the Mississippi State University Office of Research Development for a Research Initiation Program Grant. VPK specifically thanks Dr. Benjamin Matthews for his support.

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19 Genetically Modified Soybean for Insect-Pests and Disease Control Maria Fatima Grossi-de-Sa1,2, Patrícia B. Pelegrini1 and Rodrigo R. Fragoso3 1Laboratório

de Interação Molecular Planta-Praga, Embrapa Recursos Genéticos e Biotecnologia, Brasília – DF, 2Universidade católica de Brasília, Brasília – DF, 3Embrapa Cerrados, Planaltina – DF, Brazil

1. Introduction Since 1996, the cultivation of genetically modified (GM) crops around the world has increased more than 80-fold. In 2009, it was registered that a total area of 134 million hectares in 25 countries were used for biotech crops, which constituted a 7 % increase from 2008. 14 million farmers, of whom 90% were small producers, grew GM cultivars in 25 countries during 2009. Nowadays, GM soybean, cotton and corn correspond to 99% of all GM cultivars planted worldwide (JAMES, 2009). In the same year, with regard to the soybean crop, the planted area reached 90 million hectares worldwide, of which 69 million were GM. The world leaders of soybean production are the United States (33%), Brazil (27%) and Argentina (21%), which are also leaders in the use of GM seeds. The eight following countries also cultivate GM soybean, seven of which are developing countries: Paraguay, South Africa, Uruguay, Bolivia, Mexico, Chile and Costa Rica (JAMES, 2009). Through this use, the GM seed market contributes to an amount of US$ 10.5 billion annually to agriculture. Furthermore, GM soybean, along with corn and cotton, yielded US$ 130 million in 2008 and US$ 143 million in 2009 to the agribusiness sector, with a 10% increase projected for 2010. Each year, countries from the European Union import no less than 40 million tonnes of raw soy products, at a cost of more than U$ 15 billion, mainly from the three biggest producers. With respect to the agronomical traits used in GM crops, herbicide and insect resistance, or a combination of both traits, are the most utilised. Nevertheless, several other characteristics are being tested, such as increased nutritional quality, dry and cold tolerance, bacteria resistance, fungal and nematode resistances. Therefore, there is an expectation for future development of innovative molecular strategies in order to generate GM plants with novel features that promote reduction of costs and contamination risks for consumers, producers and the environment by decreasing the use of agrotoxic compounds. Most of the soybean-planted area is comprised of herbicide-tolerant crops (62%), distributed across 11 countries. Transgenic soybean with multiple combined traits corresponds to 21% of all biotech crops around the world (JAMES, 2009). During 2009/2010, a second generation of GM crops appeared in the market. RReady2Yield Soybean was cultivated by 15,000

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farmers distributed across 0.5 million hectares in the US and Canada, providing a higher production than traditional soybean crops. Until January of 2009, there was no GM soybean that was resistant to insect-pests or pathogens being commercialised, although the need for this trait is extremely important. However, an insect-resistant event of GM soybean, developed in China, is in the commercial and regulatory pipeline in that country (STEIN e RODRÍGUEZ-CEREZO, 2009 – Table 1). Today, there is only one commercial GM soybean event from Monsanto, which presents only herbicide-resistance as a trait. Four other GM soybean events are in the commercial pipeline (authorised but not yet commercialised), but all present herbicide resistance genes and demonstrate no resistance against pathogenic fungi, insect-pests or nematodes. In addition, three other soybean events resistant to herbicides are in the regulatory process required for worldwide marketing. Furthermore, two GM soybean events are already at late stages of development, although not yet in the regulatory process. While Monsanto is developing a soybean resistant to insect-pests, Syngenta is leading the development of a cultivar resistant to the cyst nematode (STEIN e RODRÍGUEZ-CEREZO, 2009 – Table 1).

2. Major soybean pests and diseases Soybean plants originate from the South Asia region, where several microorganisms and insects evolved ecological interactions. All three major soybean-producing countries in the world are located on the American continent, where more than 50% of all soybeans are harvested. This geographic distribution facilitates the spread of insect-pests and diseases. Hence, soybean can be attacked by many different organisms, ranging from viruses to nematodes and insects. These pathogens and pests can cause damage in seeds, roots, leaves, stems and pods, and usually are tissue-specific. Here, we describe some of the most important diseases that attack soybeans, as well as the major pests and pathogens, mainly found in North and South America. A list of pathogens, the diseases they cause and the infected tissues are presented in Table 2. 2.1 Seedling diseases Diseases that affect soybean seeds occur before germination or after seedling establishment. The primary reason for this is the presence of wet and/or cool soil, which enables the growth of pathogenic fungi, such as Fusarium spp, Rhizoctonia solani, Phytophthora sojae and Phythium spp. In some locations, Macrophomina spp, Coletotrichum spp and Phomopsis spp can cause this type of damage in soybean seeds. Seedling diseases can decrease seed quality, due to variation in stands, which are formed by lesions on the cotyledons, soft young stems and primary leaves. Furthermore, these diseases can retard seedling growth, causing moderate to severe losses in crop yield and, in turn, a significant increase in the use of fertilisers and herbicides (MALVICK, 2007). 2.2 Leaf pests and diseases There are several organisms that attack soybean leaves, including viruses, bacteria, fungi and insects. Among the diseases that have already been described, the most important are soybean rust, septoria brown spot, bacterial blight, bacterial pustule, downy mildew, cercospora leaf blight, frogeye leaf spot, powdery mildew, soybean mosaic virus and bean pod mottle virus.

Table 1. GM soybeans from Advanced R&D Pipeline to Commercial Events. MON87754 n/a

Monsanto Dow Agrosciences Syngenta Bayer CropScience Bayer CropScience

n/a

Monsanto

Advanced R&D Pipeline Advanced R&D Pipeline Advanced R&D Pipeline Advanced R&D Pipeline Advanced R&D Pipeline Advanced R&D Pipeline

Advanced R&D Pipeline

Herbicide tolerance (to HPPD inhibitors) Herbicide tolerance (to HPPD inhibitors) Herbicide tolerance (to HPPD inhibitors)

n/a

n/a

n/a

Crop composition (Stearidonic acid content) Herbicide tolerance (to dicamba) Crop composition (high oleic content) Herbicide tolerance

Herbicide tolerance (to glyphosate) Herbicide tolerance (to glufosinate) Herbicide tolerance (to glufosinate) Herbicide tolerance (to ALS inhibitors and glyphosate) Crop composition (High oleic content) Herbicide tolerance (to imidazolinone)

Herbicide tolerance (to glyphosate)

Insect resistance and herbicide tolerance Nematode resistance

Insect resistance

Trait

MON87769

CV127

BASF Plant Science and Embrapa Monsanto

Regulatory Pipeline

305423

Pioneer Hi-Bred

356043

Regulatory Pipeline

Authorised but not yet commercialised

A5547-127

A2704-12

Bayer CropScience Bayer CropScience Pioneer Hi-Bred

Commercial Crop

MON89788

Monsanto

Authorised but not yet commercialised Commercial Crop

MON 40-3-2

n/a

Syngenta

Monsanto

n/a

Gna

n/a China Monsanto

Event Name/Genes

Developer

Commercial crop

Advanced R&D Pipeline Advanced R&D Pipeline

GM “event” to the market Regulatory Pipeline

Planned 2015

Planned 2015

-

-

Phase 3

Phase 3

Phase 3

-

-

-

-

-

-

-

-

Development stage Pre-production trials Phase III

2015

2015

2014

2013

2014

2012

2012

2011

2010

2011

2012

2009

2009

Commercialised

2011

2013

Possible commercialisation 2010

Japan

USA(P), Brazil(P), Argentina(P), Canada(P) USA(P), Brazil(P), Argentina(P), Canada(P)

Japan

-

-

-

Japan

-

USA(T), Brazil(T), Argentina(T), Canada(E) USA(T) USA(P)

Japan

China(Pl), Japan(A), Mexico(Pl)

Japan

Japan

Japan

Japan

Japan

China, Japan, Mexico

China(A)

Net Importer

USA(T)

USA(A), Brazil(A), Argentina(Pl), Canada(A)

USA (A), Canada(E)

USA, Canada, Brazil (A), Argentina (P) USA, Argentina (P), Canada USA, Canada (E)

USA, Brazil, Argentina, Paraguay, Canada, Uruguay USA, Canada

Brazil

Net Exporter

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A=Assessment; (P) = Pending; (E) = Expected; (P) Planned; (T) Tested. Source: Stein and RodríguezCerezo (2009) with modifications

Table 2. Soybean diseases.

Bud blight

Soybean dwarf Soybean mosaic Soybean severe stunt

Soybean crinkle leaf

Peanut mottle Peanut stripe Peanut stunt Soybean chlorotic mottle

Mungbean yellow mosaic

Brazilian bud blight Cowpea chlorotic mottle

Diseases Alfalfa mosaic Bean pod mottle Bean yellow mosaic

Species Alfalfa mosaic virus (AMV) Bean pod mottle virus (BPMV) Bean yellow mosaic virus (BYMV) Tobacco streak virus (TSV) Cowpea chlorotic mottle virus (CCMV) Mungbean yellow mosaic virus (MYMV) Peanut mottle virus (PeMoV) Peanut Stripe virus (PStV) Peanut Stunt virus (PSV) Soybean chlorotic mottle virus (SbCMV) Soybean crinkle leaf virus (SCLV) Soybean dwarf virus (SbDV) Soybean Mosaic virus (SMV) Soybean Severe Stunt virus (SSSV) Tobacco ringspot virus (TRSV)

Viruses

Bacterial wilt Wildfire

Diseases Bacterial blight Bacterial pustules Bacterial Tan Spot

Species Pseudomonas amygdale, P. syringae Xanthomonas campestris pv. glycines Corynebacterium flaccumfaciens pv. flaccumfaciens Curtobacterium flaccumfaciens pv. flaccumfaciens Pseudomonas solanacearum Pseudonomas syringae pv. tabaci

Bacteria Species Alternaria spp Colleotricum truncatum, C. dematium f. truncatum, C. destructivum, Glomerella glycines, Arkoola nigra Thielayopsis basicola, Chalara elegans Septoria glycines, Mycosphaerella usoenskajae Phialophora gregata Macrophomina phaseolina Choanephora infundibulifera, C. trispora Rhizoctonia solani, Thanatephorus cucumeris, Phythium aphanidermatum, P. debaryanum, P. irregular, P. myriotylum, P. ultimum Peronospora manshurica Dreshslera glycines Cercospora sojina Fusarium spp Leptosphaerulina trifolii

Fungi

Sudden death syndrome Target spot Yeast spot

Stemphylium leaf blight

rot Rust Scab Sclerotinia stem rot Southern blight Steam canker

Pakospora pachyrhizi Spaceloma glycines Sclerotinia sclerotiorum Sclerotium rolfsii, Athelia rolfsii Diaporthe phaseolorum, Phomopsis phaseolis Stemphylium botryosum, Pleospora tarda Fusarium solani f.sp. glycines Corynespora casiicola Nematospora coryli

Downy mildew Drechslera blight Frogeye leaf spot Fusarium root rot Leptosphaerulina leaf spot Mycoleptodiscus root rot Mycoleptodiscus terrestris Neocosmospora stem rot Neocosmospora vasinfecta, Acremonium spp Phomopsis seed decay Phomopsis spp Phytophotora root and Phytophthora sojae stem rot Phyllosticta leaf spot Phyllosticta sojaecola Phymatotricum root rot Phymatotricum omniyorum Pod and stem blight Diaporthe phaseolorum, Phomopsis sojae Powdery mildew Microsphaera diffusa Purple seed stain Cercospora kikuchii Pyrenohcaeta leaf spot Pyrenochaeta glycines Pythium aphanidermatum, P. Pythium rot debaryanum, P. irregulare, P. myriotylum, P. ultimum Cylindrocladium crotalariae, Red crown rot Calonectria crotalariae Dactuliochaeta glycines, Read leaf blotch Dactuliophora glycines Rhizoctonia solani, Thanatephorus Rhizoctonia aerial blight cucumeris Rhizoctonia root and stem Rhizoctonia solani

Damping-off

Brown stem rot Charcoal rot Choanephora leaf blight

Brown spot

Black root rot

Diseases Alternaria leaf spot Anthracnose Black leaf blight

Stubby root nematode Stunt nematode

Species Hoplolaimus Columbus, H. galeatus, H. magnistylus Pratylenchus spp Paratylenchus projectus, P. tenuicaudatus Rotylenchulus reniformis Criconemella ornate Meloidogyne arenaria, M. hapla, M. incognita, M. javanica Hemicycliophora spp Heterodera glycines Helicotylenchus spp Belonolainus gracilis, B. longicaudatus Paratrichodorus minor Quinisulcius acutus, Tylenchorhynchus spp

Nematodes

Sheath nematode Soybean cyst nematode Spiral nematode Sting nematode

Reniform nematode Ring nematode Root-knot nematode

Diseases Lance nematode Lesion nematode Pin nematode

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Fungi are the most common soybean pathogens, and include Phakopsora pachyrhizi and P. meibomiae, which are the main agents of soybean rust, a disease that causes up to 50% of all crop losses in the United States and Mexico. Moreover, Septoria glycines is the agent responsible for brown spot, a common disease characterised by defoliation following the formation of brown to red lesions, which affects not only trifoliolate leaves, but also primary leaves and cotyledons (DORRANCE et al., 2008; LUSTER et al., 2010). The fungal-like Peronospora manshurica is a worldwide pathogen that causes reduction of seed size and quality due to defoliation, leading to a disease called Downy Mildew (WYLLIE e WILLIAMS, 1965). When soybean plants contaminated with Soybean Mosaic Virus (SMV) or the Bean pod mottle virus (BPMV), plants become susceptible to other pathogenic agents, especially fungi, which increase damage in the plant and treatment becomes more difficult. Virus diseases can affect seedlings, reducing their quality and germination growth. A decrease in yield is also observed, being less or greater depending on the gravity of the infection (GOLDBACH et al., 1995). Bacterial infections are widespread diseases that occur mainly in the mid-to-upper and young leaves of the soybean plant. The bacterial blight and bacterial pustules are the most common diseases described thus far. While the first of these infections is characterised by the presence of blight lesions, the second shows light green spots on the surface of the leaves. Both diseases are very similar, rarely causing defoliation or yield loss (WRATHER et al., 2001; YANG, 1997). Insect-pests are also significant predators of soybean leaves. Some Lepidoptera species, such as Anticarsia gemmantalis, known as the velvet caterpillar, cause damage in soybean plants, leading to defoliation (SWAN e PAPP, 1972). Spodoptera species also attack the leaves of soybean plants, although they are not tissue-specific and can spread to the pod as well as the entire plant in severe cases. Moreover, some Coleoptera insects have chosen soybean as one of their main target (MUSEUM, 2007). Hence, the Mexican bean beetle (Epilachna varivestis) and the Spotted cucumber beetle (Diabrotica speciosa) can cause defoliation, decreasing soybean quality and production (CAB/EPPO, 2003; CRANSHAW, 2004). Soybean insectpests are described in Table 3. 2.3 Stem-and-Pod pests and diseases Fungi and insects are the major causes of stem and pod damage in soybean. Anthracnose is one of the most well-known fungal diseases, and it attacks soybean plants during wet and warm conditions. Colleotricum spp and Glomerella glycines may not cause significant yield loss, but they can reduce stand and seed quality (MACHADO e NETO, 2003). Some regions of the U.S. and Canada are commonly attacked by Sclerotinia stem rot (Sclerotinia sclerotiorum) every year (PURDY, 1979). In contrast to Anthracnose, these fungi appear during cool weather and resistant varieties of soybean are already being developed. The insect-pests that target pods and stems in soybean cultivars are varied. Lepidoptera, Coleoptera and Hemiptera species contribute to a decrease in plant production and loss of quality (Table 3). In this way, Helicoverpa zea is an insect-pest widely distributed across North and South America that has been recently introduced to the Hawaiian Islands. This pest initially eats soybean leaves but can later attack pods, causing serious yield damage (HARDING, 1976). Furthermore, larvae of the soybean stem borer Dectes texanus texanus feed within young soybean plants, resulting in a small yield loss. However, severe stem damages from the inside of the plant can threaten an entire plantation (CAMPBELL e DUYN, 1977).

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Lepidoptera

Soybean tissue attacked Leaf

Popular Name

Scientific Name

Green cloverworm Soybean Looper Velvetbean Caterpilar Saltmarsh caterpillar Yellowstriped armyworm

Plathypena scabra Pseudoplusia includens Anticarsia gemmatalis Estigmene acrea Spodoptera ornithogalli

Lower leaves to the whole plant Leaf and Pod

Beet armyworm

Pod

Corn Earworm Silverspotted skipper

Spodoptera exigua Spodoptera latifascia Spodoptera eridania Helicoverpa zea Epargyreus clarus

Stem and Plantlet Coleoptera

Leaf

Mexican Bean Beetle Bean Leaf Beetle Spotted Cucumber Beetle Blister Beetle Japanese Beetle

Stem and Plantlet

Soybean stem borer Grape colaspis

Leaf eating beetle Root Hemiptera

Pod

Green Stink Bug Brown Stink Bug

Root

Orthoptera

Stem and Plantlet

Threecornered alfalfa hopper

Leaf

Grasshopper

Table 3. Soybean Insect-pests

Elasmopalpus lignosellus Epinotia aporema Epilachna varivestis Cerotoma trifurcata Diabrotica undecimpunctata howardi Diabrotica speciosa Epicauta pestifera, Epicauta lemniscata Aracanhtus mourei Popillia japonica Maecolaspis calcarifera Megascelis sp. Dectes texanus texanus Colaspis brunnea Sternechus subsignatus Chalcodermus sp Myochrous armatus Blapstinus sp Phyllophaga cuyabana Lyogenis suturalis Acrosternum hilare, Nezara viridula Euschistus servus Euchistus heros Piezodorus guildinii Maruca testulalis Etiella zinckenella Scaptocoris castanea Atarsocoris brachiariae Dysmicoccus sp Pseudococcus sp Spissistilus festinus (Dichelops melacanthus e Dichelops furcatus Thyanta perditor Melanoplus spp

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2.4 Root pests and diseases Although several fungi and insect species can damage soybean roots, nematodes are the most nocive and well-studied organisms that cause significant yield losses in this crop (Tables 2 and 3). Sedentary phytonematodes are divided into three classes: Globodera, Heterodera and Meloidogyne, and are the most nocive plant pathogens worldwide, causing losses of US$ 125 billion annually in agriculture (CHITWOOD, 2003; SASSER, 1980). Parasitic nematodes are responsible for 12%-20% of all crop losses (KOENNING et al., 1999; SASSER e FRECKMAN, 1987). Worldwide losses caused by nematodes from the Meloidogyne genus can reach US$ 16.5 billion per year in some cultivars (TRUDGILL e BLOK, 2001). Among these, Meloidogyne incognita is probably the most important nematode for agriculture, due to its worldwide distribution and the wide range of plants that it attacks (EHWAETI et al., 1999; TRUDGILL e BLOK, 2001). In addition, this species is responsible for 95% of nematode infestations around the world. By comparison, species from the genus Heterodera cause estimated annual losses of US$ 430 million in the US and US$ 95 million in Europe (MULLER, 1999; SASSER et al., 1983; WRATHER et al., 1997). The soybean cyst nematode (Heterodera glycines) is a widespread pathogen that can be under the soil for years before it can be identified and the host eliminated. It usually causes nonevident symptoms, making diagnosis difficult. This nematode enters the plant root and starts to feed, not visible to the farmers. Consequently, soybean plant losses from this pathogen can reach 30% due to the late diagnosis and difficulties associated with pathogen control (ICHINOHE, 1988). In addition, root-knot nematodes, represented by many species of Meloidogyne, are worldwide-distributed plant-parasites that feed on soybean roots, causing an average of 5% of crop loss around the world (SASSER e CARTER, 1985). They are most common in warm and moist soils, can easily be introduced into soybean roots and are difficult to control (EISENBACK e TRIANTAPHYLLOU, 1991; STIRLING et al., 1992). In Brazil, the nematode Pratylenchus brachyurus can cause losses of 30-50% in some soybean fields. Their life cycle is very short, ranging from 3 to 4 weeks, which enables them to reproduce quickly and present many generations during the same cultivation period. Therefore, the control of P. brachyurus is very difficult, once that nematicides and culture rotation are inefficient strategies for the elimination of this plant parasite.

3. Biotechnological strategies to circumvent pests and diseases 3.1 Bt soybean: a future tool on insect-pests control In nature, there are roughly 100 known bacterial species with potential for insect pathogenesis, but only a few have succeeded as bio-insecticides (ROWE e MARGARITIS, 2004). Among them, formulations based on Bacillus thuringiensis (Bt) have showed an effective activity against mosquito larvae and the insecticide activity of this bacteria is due to proteins produced during sporulation, which include a crystal complex (FEDERICI, 2005). Insects from the Lepidoptera order are particularly susceptible to Cry1 toxins from B. thuringiensis (Bt toxins), which are highly toxic after ingestion. They are soluble and are processed by proteases presented in the insect midgut. The active toxins can interact with specific receptors at the midgut, leading to pore formation after toxin conformation change and, consequently, cell death (GILL et al., 1992; KNOWLES, 1994; RAJAMOHAN et al., 1998). Although this is the most commonly suggested method of action for Cry toxins, details about this mechanism, such as pos-binding effects and receptor specificities, are not yet clear (GOMEZ et al., 2002; ZHANG et al., 2005).

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In this way, genetic transformation of soybean to induce resistance to lepidopterans using the insertion of Bt toxins has been performed for over a decade. Hence, many techniques have been applied in order to successfully transfer Cry genes into soybean embryos. The bombardment method has provided stable transformed soybean plants (CHRISTOU, 1990; MCCABE e CHRISTOU, 1993). However, the use of Agrobacterium for transformation of cotyledonary nodes and the microprojectile bombardment of somatic embryos provided the growth of less fertile transformed plants (FINER e MCMULLEN, 1991; PARROTT et al., 1994; SATO et al., 1993; TOWNSEND e THOMAS, 1993). Nevertheless, years later, it was possible to produce a fertile transformed soybean containing a synthetic B. thuringiensis insecticidal crystal protein gene (Cry1Ac) through microprojectile bombardment. These plants showed resistance toward Helicoverpa zea, the soybean looper Pseudoplusia includes, the tobacco burworm (Heliothis virescens) and the velvetbean caterpillar (Anticarsia gemmatalis) (STEWART et al., 1996). In order to increase plant resistance against insect-pests, pyramiding strategies were applied in soybean using a synthetic Bt toxin (Cry1Ac) with native plant resistance genes. Two QTLs from Japanese soybean lines, named 229-H and 229-M, have been described as showing antixenosis and antibiosis resistance towards lepidopteran insects (CREGAN et al., 1999). Hence, a GM soybean presenting a combination of 229-M from the strain PI 22948 and a synthetic cry1Ac gene demonstrated resistance against H. zea, P. includens, A. gemmatalis and Elasmopalpus lignosellus (WALKER et al., 2000). Further studies utilised both QTLs, 229-H and 229-M, with the synthetic cry1Ac gene for production of transgenic soybean, which showed to be resistant against three lepidopteran insect-pests (H. zea, P. includes and H. virescens) (WALKER et al., 2004). Later, new reports described the production of a GM soybean containing a third QTL, named QTL-G, along with the cry1Ac resistance gene. It was showed that QTL-M presented the largest effect on P. includens and H. zea resistance. QTL-G worked better against H. zea larvae (RECTOR et al., 2000; ZHU et al., 2008), while QTL-H was not as effective when compared to the other two resistance genes (WALKER et al., 2004; ZHU et al., 2008). Therefore, the addition of another QTL, in order to increase insect resistance, represents an interesting strategy for biological control in soybean cultivars (ZHU et al., 2008). Recently, transgenic lines of soybean expressing the B. thuringiensis toxin Cry1Ac were tested in the field for their potential resistance against lepidopteran pests. It was demonstrated that Bt toxins could also be used in soybean as a resistance methodology aimed towards A. gemmatalis, P. includens, and Hypena scabra (MCPHERSON e MACRAE, 2009). 3.2 Digestive enzyme inhibitors The occurrence of proteinase inhibitors (PIs) as defence-related proteins and their role on plant protection are well described in the literature. Since 1947, it has been observed that PIs from soybean were able to inhibit the growth of insect-pests larvae, including the coleoptera Tribolium confusum (HAQ et al., 2004; LYSON, 2002; MICKEL e STANDISH, 1947). Later, in vitro and in vivo bioassays demonstrated that protease inhibitors were also active against other insect species, such as Anagasta kuehniella, Hypera postica and Anthonomus grandis (FRANCO et al., 2003; MACEDO et al., 2003; WILHITE et al., 2000). Hence, as there is no evidence that proteinase inhibitors have toxic or deleterious effects on mammals, they constitute a significant alternative for the development of transgenic crops resistant to insect-pests and nematodes. Soybean cultivars are predated by several insect species, requiring the application of different insecticide compounds in agriculture. Proteinase inhibitors, such as AKTI (Albizzia

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kalkira proteinase inhibitor), could, therefore, be a promising choice, as it has been shown to reduce the activity of the beet armyworm S. exigua by 57% (ZHOU et al., 2008). Approaches for the development of genetically engineered soybean lines, which express insecticidal molecules, are also being widely studied. Transgenic tobacco expressing a cowpea trypsin inhibitor (CpTI) showed activity against Heliothis virescens larvae (GATEHOUSE et al., 1993), as well as species from Diabrotica spp and Spodoptera spp (HILDER et al., 1987). However, it was shown that in Nicotiana tabacum transgenic plants expressing the barley trypsin inhibitor BTI-CMe, although larvae of Spodoptera exigua were susceptible to the proteinase inhibitor, they were able to produce alternative proteins that could maintain survivance of the insect-pest (LARA et al., 2000). This report demonstrated the adaptation development of S. exigua over genetically modified tobacco cultures. The inhibition of nematode digestive enzymes is one type of anti-feeding strategy, exemplified by plant transformation with genes encoding proteinase inhibitors (ATKINSON et al., 2001; LILLEY et al., 1999a; LILLEY et al., 1999b). To this end, there are reports that describe the characterisation of proteinase activities in crude protein extracts of plant-parasitic nematodes (LILLEY et al., 1996; MICHAUD et al., 1996) or the isolation of their proteinase genes (FRAGOSO et al., 2005; FRAGOSO et al., 2009; LILLEY et al., 1997; NEVEU et al., 2003; URWIN et al., 1997a). For example, the production of a genetically modified Arabidopsis thaliana with a gene encoding the modified proteinase inhibitor from rice, cystatin Oc-1 delta D86, was able to inhibit growth of Heterodera schachtii and Meloidogyne incognita females, blocking egg production and pest proliferation. Other reports showed the potential application of anti-feeding strategies based on plant transformation to express proteinase inhibitors (ATKINSON et al., 2001; URWIN et al., 1997b; URWIN et al., 1998), proteinase gene silencing by RNAi (URWIN et al., 2002), in vitro inhibition of cysteine proteinase activity using the cognate pro-region of nematode cysteine proteinase (SILVA et al., 2004) and the transformation of soybean roots to express the propeptide (MARRA et al., 2009). 3.3 Defensins: small tools against insect resistance Defensins are antimicrobial peptides varying from 45-54 amino acid residues stabilised by 34 disulfide bridges, which are isolated from different sources, such as plants, mammals, insects and crustaceous (THOMMA et al., 2002). They have been described as important tools for the control of pathogenic fungi, especially Rhizoctonia solani and Fusarium species, which are important organisms that causes damage in soybean cultivars (OLLI e KIRTI, 2006; WANG e NG, 2007). Therefore, the study of defensins may lead to the development of future transgenic plants encoding peptides toxic to these phytopathogenic fungi. It was previously demonstrated that a recombinant defensin from mungbean (rVrD1) was able to inhibit the growth of R. solani and F. oxysporum (CHEN, 2004). The same fungi also had their activity inhibited by another defensin-like peptide, called coccinin, isolated from the seeds of large scarlet runner beans (Phaseolus coccineus) (Ngai et al., 2004). Furthermore, Tfgd1, a recombinant defensin from the legume Trigonella foenum-graecum, demonstrated similar features, inhibiting the growth of R. solani at a low concentration (OLLI e KIRTI, 2006). In addition, F. oxysporum growth was strongly affected by the recombinant Vitis vinifera antimicrobial peptide (Vv-AMP1), as well as by the peptide from the flowers of Nicotiana alata, NaD1 (BEER e VIVIER, 2008; WEERDEN e ANDERSON, 2008). Psd1, a defensin isolated from pea seeds, was also able to decrease growth of the phytopathogen F. solani by interacting with the fungus membrane, leading to cell death (MEDEIROS et al., 2010). Many other defensins have been characterised as having antifungal activities, and these can be observed in Table 2.

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Studies on the expression of defensins in transgenic model plants are confirming the activity of foreign peptides as an important tool against soybean pathogenic fungi. Hence, using Arabidopsis thaliana as an expression system, defensins PDF1.1 and PDF1.3 were produced, purified and analysed against several fungi species, showing activity against two soybean pathogens, F. graminearum and F. oxysporum (SELS et al., 2007). 3.4 Using spider and scorpion venom toxins as novel insecticidal mediators Scorpion and spider venoms are being studied for their several physiological and pharmacological effects against pests and pathogens (ROCHAT et al., 1979; ZLOTKIN et al., 1991). Scorpion toxins seem to have specificity to some insects that cause economic crop losses around the world, including soybean loss. Therefore, such toxins are being evaluated for use in future development of recombinant biopesticides as an alternative strategy to control insect-pests. Several insect-related toxins have been identified from scorpions, isolated from diverse geographical locations (BECERRIL et al., 1995; CREST et al., 1992; NAKAGAWA et al., 1997; ZLOTKIN et al., 1991). In this manner, toxins purified from the venom of the spider Plectrurys tristis demonstrated insecticidal activity against H. virescens, an insect-pest of soybean cultivars (QUISTAD and SKINNER, 1994). Moreover, a toxin identified from the venom of Mesobuthus tumulus, called ButaIT, showed high antagonistic activity towards the insect H. virenscens (WUDAYAGIRI et al., 2001). The interest in developing insecticidal compounds for field application in order to reduce the use of chemical agrotoxics and decrease insect resistance has led to new biotechnologybased approaches. Although not yet used in soybean plants, the insertion of a gene encoding toxin into the genome of a plant or baculovirus is a potential alternative (KHAN et al., 2006; LIMA et al., 2007; STEWART et al., 1991). The fusion of a toxin to the N-terminal of insect lectins has also given successful results. Lectins can act as carrier proteins and direct the fused toxins to the insect haemolymph, causing death (PHAM TRUNG et al., 2006). 3.5 Using gene silencing for nematode control One of the techniques utilised in soybean biotechnological products is the genetic transformation of cultivars expressing double strand RNAs (dsRNA) in order to drive gene silencing in nematodes. The mechanism of pos-transcription gene silencing using dsRNA is known as RNA-mediated interfering (RNAi), or gene silencing (BOSHER e LABOUESSE, 2000; HUNTER, 2000; KUWABARA e COULSON, 2000; SHARP, 1999). Gene silencing can be either partial – called knckdown – or total – denominated knockout. Briefly, the RNAi mechanism uses DICER complexes (dsRNA-specific RNase III-type endonuclease), which recognise and digest the dsRNA into siRNAs (small interfering RNAs) that ranges from 21-26 base pairs. These siRNAs flow through four possible pathways. First, siRNAs bind to the RISC complex (RNA-induced silencing complex) to search for and destroy complementary mRNA. Second, siRNAs bind to the RdRp complex (RNA-dependent RNA polymerase), priming mRNAa to synthesise new dsRNAs, and potentialising the entire RNAi process. Third, siRNAs bind to the membrane protein complex SID (systemic RNAi), which spreads out siRNAs to neighbourhood cells and, probably, to all cells, generating a systemic response of gene silencing. Fourth, siRNAs bind to the RITS complex (RNA-induced transcriptional silencing), which drives heterochromatin condensation of homologous regions, directing gene promoter turn-off and, consequently, pre-transcriptional gene silencing.

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Researchers have applied RNAi technique in order to evaluate the effect of RNAi on nematode development. This method was already widely used in the free-living nematode Caenorhabditis elegans, and its effect is normally gene-specific, systemic, lasting, or even hereditary (ALDER et al., 2003; BARGMANN, 2001; BARSTEAD, 2001). Several reports use the methodology of in vitro dsRNA production and administration of nematodes by incubation (soaking), followed by plant infection for the determination of parasitism, such as the levels of reproduction and survival. Some silenced target genes showed high potential for use in parasitism control, such as: (i) proteinase genes in cyst nematodes Heterodera glycines and Globodera pallida (URWIN et al., 2002), and root-knot nematode M. incognita (SHINGLES et al., 2007); (ii) chitin synthase in M. artiellia (FANELLI et al., 2005); (iii) aminopeptidase in H. glycines (LILLEY et al., 2005); (iv) cellulases in G. rostochiensis (CHEN et al., 2005) and H. glycines (BAKHETIA et al., 2007); (v) amphideal secretion protein in G. rostochiensis (CHEN et al., 2005); (vi) FMRF-like peptides in G. pallida (KIMBER et al., 2007); (vii) pectate lyase, corismato mutase and secretion peptide SYV46 in H. glycines (BAKHETIA et al., 2007); and (viii) double oxidase in M. incognita (BAKHETIA et al., 2005). Another strategy commonly applied is the use of transformed plants that express nematode target-gene dsRNAs and their evaluation in the presence of nematode infection. In this way, some plants showed partial-to-complete nematode resistance, as observed for the peptide 16D10 dsRNA expressed in Arabisopsis thaliana increasing resistance against M. incognita, M. javanica, M. arenaria and M. hapla (HUANG et al., 2006). Moreover, the major sperm protein dsRNA showed increase of soybean resistance towards H. glycines in soybean (STEEVES et al., 2006). Reports also describe the successful use of this technique, such as in the silencing of M. incognita genes that encode a splicing factor and an integrase. After nematodes were fed with RNAi transformed tobacco roots, they showed smaller and fewer healthy females when compared to those that were fed the control plants (YADAV et al., 2006). Later, it was demonstrated that the exposure of nematodes to RNAi fragments increased their susceptibility to RNAi delivery, confirming the importance of gene silencing techniques as biotechnological tools to improve plant resistance to pathogenic nematodes (LILLEY et al., 2007). Earlier studies also reported that the silencing of a gene encoding cathepsin L-cysteine (mi-cpl-1) from M. incognita was able to decrease the number of fertile females. In this case, the production of eggs was reduced to 40% using only a small fragment of the respective gene (800 bp) (SHINGLES et al., 2007). Moreover, studies of the transcription factor of M. javanica in tobacco plants produced satisfactory results, showing that the RNAi technique is an excellent strategy to study plant resistance to phytopathogenic nematodes (FAIRBAIRN et al., 2007). Recently, it was reported that the use of four different RNAi gene-silencing constructs was able to decrease cyst nematodes in transformed soybean roots (KLINK et al., 2005; KLINK et al., 2009). Furthermore, another study showed that silencing of a tyrosine phosphatise gene and a mitochondrial stress-70 protein precursor from M. incognita provided the reduction of gall formation in transformed soybean. It also revealed that nematode development was affected by RNAi constructs (IBRAHIM et al., 2010). Target-specific RNAi of the H. schachti gene Hg4F01, a related species of H. glycines, expressing an annexin-like protein was also studied in order to identify its function on nematode resistance. H. schachti was used in this work because of its effect on Arabidopsis thaliana, which could not be performed with H. glycines. Hence, using plant-host derived RNAi and the Arabidopsis–cyst nematode system, it was demonstrated that the annexin-like protein had a significant effect on plant-nematode interactions. In addition, it was concluded

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that the expressed protein, which is also produced in Arabidopsis, may mimic plant annexin function (PATEL et al., 2010).

4. Directing transgene expression to attacked tissues 4.1 Promoter isolation and characterisation Once the Cauliflower Mosaic Virus (CaMV) 35S promoter was isolated and characterised (ODELL et al., 1985), it became the major general-purpose and most widely-used promoter in GM plants (HERNANDEZ-GARCIA et al., 2009). CaMV 35S is the promoter of choice in more than 80% of GM plants (HULL et al., 2002), because it drives constitutive high levels of transgene expression (VENTER, 2007). Despite the great success of the 35S promoter, there is a scientific interest in discovering new promoters with differentiated and specialised functions. Moreover, the stability and expression pattern of foreign genes driven by the 35S promoter has been tested and questioned (BAKHSH et al., 2009; WESSEL et al., 2001). Therefore, the discovery of plant promoters was essential to drive predictable temporal and tissue-specific expression patterns and high levels of protein production (LU et al., 2008). When evaluating transgenic strategies for nematode control, although widely used, the 35S promoter has certain limitations, such as its poor performance in monocots, and its suppression when feeding nematodes (URWIN et al., 1997c). Plant promoters used in biotechnology are divided into three categories based on gene expression pattern: constitutive (almost everywhere, every time); spatiotemporal (tissuespecific and/or stage-specific); and inducible (regulated by some specific signal) (POTENZA et al., 2004). Inducible promoters can fit into three categories: endogenous-signal responsive (plant hormones); external, physical-stimuli responsive (abiotic and biotic stresses); and external, chemical-stimuli responsive (PEREMARTI et al., 2010). Plants have evolved defence mechanisms to combat pests and pathogens. Some defence mechanisms are innate, others are only active when and where there is an attack. These mechanisms, therefore, are dependent on biotic-interaction detection, functionality of signalling pathways, defence-gene expression, cellular- and tissue-defence activation. The expression of defence genes is driven by chemical-inducible promoters, when elicitors produced by pests and pathogens are recognised by plants (PEREMARTI et al., 2010). Similarly, pest wounding generates physical signals which direct expression of defence genes. Both elicitor-inducible and wound-inducible promoters are relatively well-conserved across taxonomic groups, implying wide plant species spectra in transgenic strategies (PEREMARTI et al., 2010). Plant cellular response to biotic stress - initiated by biochemical signalling cascades that detect pest injury or pathogen invasion - can be carried out by activation and/or inhibition of transcription factors (trans-elements). In turn, they bind to specific DNA sequences (ciselements) to regulate gene expression. In this way, resistance mechanisms are activated only, or mainly, when and where there is an attack, and achieve a lethal dose. Besides identifying genes to control pests and diseases, the use of such biotic-stress induced promoters could ensure transgene expression primarily in affected tissues, specific to a specific targeted pest or pathogen. Root-knot and cyst nematodes induce plant cell differentiation to generate nematode feeding sites, giant cells and syncytia, respectively, which act as nutrient sinks. Such cellular modification, coordinated by a complex gene-regulation network, is under examination in

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several studies in order to find key transcription factors. Some nematode-induced promoters could represent excellent vectors for the administration of lethal doses to nematode feeding sites in infected roots. Hence, a soybean heat shock protein 90-like promoter (GmHSP90L) and a soybean polyubiquitin promoter (Gmubi) have shown higher expression levels of the gfp gene than the cauliflower mosaic virus 35S promoter (CaMV35S), when transformed in cotyledons of germinating lima bean (Phaseolus lunatus) seeds (CHIERA, 2007). An intronic region (Gumpri) from the Gmubi promoter seems to be essential for increasing gene expression levels. It was demonstrated that the activity of Gmubi promoter was two times higher than CaMV35S promoter when the intron was removed, compared to a five times higher when the intronic region was present. Similar results were also obtained for ubiquitin promoters from other sources. The ubiquitin promoter from rice showed no activity when the intronic region was removed (WANG e OARD, 2003). Also, The expression of the same promoter in tobacco was not only reduced when the intron was not present, but the tissue-specific expression was altered (PLESSE et al., 2001). Moreover, the GmHSP90L promoter showed stronger expression levels than CaMV35S, but the expression level decreased after promoter truncation, revealing that this region might be related to GmHSP90L promoter regulatory elements (CHIERA, 2007). Detailed studies of gene expression at nematode feeding sites have been performed by several research groups, and some nematode-induced promoters have been identified and characterised. The TobRB7 gene (enconding a water channel protein - OPPERMAN et al., 1994), the Hahsp17.7G4 gene - which encodes a heat shock protein (ESCOBAR et al., 2003) and the E2 gene (enconding the ubiquitin conjugation factor 2 - BIRD, 1996), are al highly expressed in the giant cells of M. incognita. Ubiquitination plays important roles, such as on mediating lipidation, protein activity regulation, protein–protein interaction control, subcellular localisation (MUKHOPADHYAY e RIEZMAN, 2007), transcription control through histone ubiquitination, translation control, DNA repair, regulation of endocytosis and protein trafficking (MURATANI e TANSEY, 2003). Indeed, ubiquitination regulates various aspects of plant life, including disease resistance (ZENG et al., 2006), hormone signalling (ITOH et al., 2003), many developmental processes and cell cycle control (MOON et al., 2004). Thus, the E2 enzyme, a member of the Leubc4 family, has a major role in cellular metabolism and giant cell formation. Because of this, its promoter region was suggested for promoter characterization and use as biotechnological tool for root-nematode control (BIRD, 1996). Therefore, the soybean E2 gene promoter, named UceS8.3, was isolated and characterised for further use in soybean genetic transformation (GROSSI-DE-SA et al., 2008). In the same way, the soybean polyubiquitin gene promoter was isolated and characterised (HERNANDEZ-GARCIA et al., 2009). Functional genomic studies have been applied to study plant-nematode interactions, in order to identify genes that were up-regulated and down-regulated at nematode feeding sites. The use of microarray hybridisation, serial analysis of gene expression (SAGE) and proteomic methodologies (FRANCO et al., 2010; MEHTA et al., 2008) achieved gene expression analyses at genomic scale, while quantitative real time PCR, in situ hybridisation and Laser Capture Microdissection techniques provided more accuracy in comparing differences in promoter expression. The use of novel promoters for gene expression control can be widely used in genetically modified soybean in order to direct the foreign gene to a specific tissue attacked by a certain pest or pathogen. In this way, some promoters identified in soybean have been studied in model plants and compared with other existing promoters from other sources.

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Hence Khan and colleagues (2004) made comparisons of 1,300 cDNAs from soybean infested with H. glycines at 2 dpi (Table 4). Klink and colleagues (2005) used laser capture microdissection to collect syncytia of soybean under 8-dpi infestation with H. glycines. A total of 800 cDNAs were sequenced, assembled as 174 consensus sequences, where 6 were up-regulated, 1 was down-regulated and 5 were both activated or inhibited, as confirmed by qRT-PCR. Later, Aklharouf and colleagues (2006), using microarray chips with more than 6,000 cDNA spots, detected hundreds of differentially expressed genes at from 6 hours (hpi) to 8 days post-infection (dpi). ITHAL and colleagues (2007a) simultaneously compared infested and non-infested soybean roots and different development stages of H. glycines at 2, 5 and 10 dpi. The microarray analysis of 35,611 soybean genes and 7,431 nematode genes detected 429 plant genes and 1,850 nematode genes with differential gene expression. ITHAL and colleagues (2007b) made microarray hybridisations to study syncytia development from 0 to 10 dpi. Using chips with 35,611 soybean genes, they observed 1,765 genes with changes in expression pattern until 2 dpi, when 1,116 genes were up-regulated, while 649 genes were down-regulated.

5. Perspectives on GM soybean for insect-pests and diseases control The glyphosate-tolerant GM soybean alone corresponds to 52% of all biotech crops planted world area. Indeed, considering soybean, herbicide tolerance has still been the major aimed trait, with around ten novel varieties showing tolerance to different chemical compounds in their final steps of R & D pipeline to commercial events. However, there is an obvious need and seed market demand for insect-pests and plantpathogens resistance traits. In a very near future, the first GM soybean resistant to insectpests and nematode will be available as single traits or together with herbicide tolerance (stacked traits). In that way, several biotechnological strategies and their candidate genes have been tested in order to induce resistance to various pests and diseases, including the tissue overexpression of genes driven by specific plant promoters. Hence, it is expected to observe, in a near future, the production of soybean and its processed products with less or none agrotoxic residues or micotoxins from opportunistic fungi, as it has been already seen by the GM maize containing a Bt toxin. Consequently, it is also expected that further GM soybean traits enable a relevant decrease of costs with chemical pesticides, the enhancement of soybean quality and crop production, as well as the maintenance of a non-poluted and biodiverse environment. Recenlty, the Academy of Science from the Vatican announced their support on the production of transgenic plants. The report was published in an International Scientific Journal and signed by 40 specialists – including 7 from the Vatican itself. Among other things, the report described the need for development of new agricultural technologies in order to decrease malnutrition and starvation that surrounds 1 billion people of the world (Potrykus et al., 2010). This announcement brought new perspectives for biotechnology, once that now religion and science are walking together for the improvement of food supply and the development of new techniques into plant transformation. Therefore, it is expected that, in future, more advanced strategies provide the cultivation of several transgenic crops with diverse resistances towards pests, pathogens and environment conditions, with the approval for consumption not only from Inspection Organizations, but from the entire society.

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Biologic process or predict gene function (Protein)

Time pos infestation (analysis technique)

▲ Defence ▲ Stress response (peroxidase; SAM22) ▲ Carbohydrate metabolism ▲ Cellular signalling ▲ Unknown ▲ Defence (SRG2) ▲ Water channel (GmPIP2,2) ▲ Cell skeletal (GmTubA1; GmTubB4) ▲ DNA binding (MYB-related protein) ▼ Protein catabolism (ubiquitin) ▲ Defence ▲ Wounding response ▲ Transcription factor (WRKY) ▲ DNA duplication ▲ RNA transcription ▲ Protein translation ▲ Unknown ▲ Defence ▲ Stress response (peroxidase) ▲ Primary and secondary metabolism ▲▼ Cell wall modification ▲ Plant development ▲ Cellular signalling ▲ Transcription factor (WRKY; bZIP; ERF; MYB) ▲ Disease related ▲ Primary and secondary metabolism ▲▼ Cell wall modification ▲ Lignin and suberin biosynthesis ▲▼ Transporter of sugar, metal ion and amino acid ▲▼ Auxin related ▲▼ Ethylene related ▼ Cytokinin related ▼ Gibberellin related ▼ Jasmonic acid biosynthesis ▲ Development related (PHAP2A) ▼ Water channel (Rb7)

2 dpi (Microarray)

(KHAN et al., 2004)

8 dpi (EST library)

(KLINK et al., 2005)

6 hpi, 12 hpi 1, 2, 4, 6 and 8 dpi (Microarray)

(ALKHAROUF et al., 2006)

2, 5 and 10 dpi (Microarray)

(ITHAL et al., 2007a)

2, 5 and 10 dpi) (Microarray)

(ITHAL et al., 2007b)

Reference

Differential expression of individual genes, relative to non-infested plants, at different times was associated to predicted gene function and biologic process. The arrows represent predicted biological process activation (▲), inhibition (▼) or undetermined (▲▼), considering the results of individual upor down-regulated genes.

Table 4. Biologic process modification during infestation of G. max plants by H. glycines.

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Part 4 Recent Technology

20 Spectral Characteristics of Soybean during the Vegetative Cycle Using Landsat 5/TM Images in The Western Paraná, Brazil Erivelto Mercante1, Rubens A. C. Lamparelli2, Miguel A. Uribe-Opazo1 and Jansle Viera Rocha2 1Western 2State

Paraná State University (Unioeste-PR) University of Campinas (Unicamp-SP) Brazil

1. Introduction There are many attempts of using alternative tools capable of providing more reliable and specific information in relation to agricultural crops. Techniques linked to remote sensing appear as high potential instrumental. The spectral-temporal profiles of crops can establish the growing standard and their development degree during the phenological cycle, because structural changes are progressive with time and result in corresponding changes in the spectral reflectance. These changes occurred in the curves particular to the spectral profiles of a specific crop so may be associated to their condition (development) and also to their efficiency. The crop monitoring by use of the spectral-temporal profile demands the atmospheric correction of the images so that it becomes possible to analyze the information under the same response unit, which means surface reflectance (Du et. al., 2002). The study of Chander & Markham (2003) shows the development of a procedure to improve the TM sensor calibration, offering Landsat 5/TM data users, methods and parameters to convert image digital numbers (DNs) to useful data, such as spectral radiance, top-ofatmosphere reflectance (apparent) and temperature estimates. The correction can be performed in two different ways: by models of radiative transfer like Modtran2 (Anderson et al., 1993), Scoradis based on the 5S (Zullo Jr, 1994) and 6S (Vermote et al., 1997), where atmosphere information is required in order to estimate the absorption and scattering effects. Another method takes into account the subtraction of grey levels of dark pixels (DOS- Dark Object Subtraction). This method has the advantage of not depending on atmospheric parameters (Teillet, 1995). Both procedures are considered absolute corrections. Researchers are working on the relative correction of images to avoid the gathering of data that feed the radiative transfer and atmospheric correction models, which are too slow and expensive to be acquired. These methods assume that the relation of the top-of-atmosphere radiation recorded in two different dates, from regions with constant reflectance, is especially homogeneous and may be approximated by a linear function (El Hajj, 2008).

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The relative calibration, also referred to as image normalization (Schroeder et al., 2006) is applied by pairs of images radiometrically related using methods of choice of pseudoinvariant pixels – PIFs (Hall et al., 1991; Schott et al. 1988). Another method of automatic treatment of temporal series was presented by Nielsen et al. (1990), who determine invariant pixels by using the canonical correlation (CCA), also called MAD (Multivariate Alteration Detection), to find the linear combination between two groups of variables (spectral bands of the result image and reference image) ordered by correlation or similarity between pairs. The MAD technique was successfully used by Canty et al., (2004), who performed the radiometric normalization of multitemporal series of images from Landsat 5/TM, Landsat 7/ETM+ and Spot HRV satellites. The authors compared the results obtained by the MAD with those obtained with the manual way of detecting invariant pixels. The results were favorable to the MAD technique in relation to the time it took to obtain the invariant pixels in extensive areas. Canty et al. (2004) used, along with the MAD technique, the orthogonal regression instead of the linear regression (generally used), to accomplish the normalization of the obtained images in different dates. The results gathered from the orthogonal regression proved to be more satisfactory, in the sense of being able to keep the radiometric resolution of the images after normalization, in other words, it did not distort the typical behavior of the present targets in the images used for the normalization, in each one of the bands of the satellite. It is noteworthy the fact that it preserved the radiometric resolution of the images after normalization, what was very much emphasized in the study of Du et al. (2002). Agricultural crop monitoring by satellite images makes use of vegetation indexes, defined as combinations of spectral data of two or more bands (Moreira, 2000). Among several vegetation indexes available, the NDVI (Normalized Difference Vegetation Index, Rouse et al., 1973) has been largely used. Crist (1985) appoints the importance of the use of the indexes resulting from the orthogonal transformation Tasseled Cap. These indexes use the six non-thermal bands of the TM sensor and attribute different weights for each one of them by linear equations. As a result, three synthesis-images are generated: “brightness”, “greenness” and “wetness”. The image “greenness”, or the vegetation index GVI (Greenness Vegetation Index), is created from the TM sensor of the Landsat 5 satellite.

2. Objective The main goal of this study was to analyse changes in the spectral behavior of the soybean crop, during the vegetative cycle, by spectral-temporal profiles of the mapped crop areas, using the vegetation indexes NDVI e GVI (Greenness Vegetation Index) of multispectral images from the Landsat 5/TM satellite.

3. Methodology Figure 1 shows the area coverage of the image and the localization of the 36 municipalities which were monitored in this study. A commercial agricultural area was also monitored, it is located next to the municipality of Cascavel, with approximately 57ha and central coordinates of latitude 24º 57’ 30” S and longitude 53º 34’ 20” W and average altitude of 650 meters. The climate in the region is presented as temperate mesothermal and superhumid, climate type Cfa – Köeppen, with moderate temperatures, well distributed rains and hot summer. In

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Fig. 1. Location of the Landsat 5/TM Path/Row 223/77 in Parana State, Brazil, with the location of the 36 municipalities monitored. the Winter months, the average temperature is lower than 16° C and in the Summer months the maximum temperatures overcome 30° C, and an annual average temperature of 21° C. The region is subjected to frost, although it is not frequent. The soil in the monitored area with approximately 57ha is classified as Dystroferric Oxisol, and the soybean variety cultivated was the Coodetec 216 (CD 216), characterized as part of the early maturation group, and an average total cycle of 112 days (Coodetec, 2007). The TM/Landsat 5 (Thematic Mapper) images were obtained at the Instituto Nacional de Pesquisas Espaciais (INPE) – (National Institute for Space Research) with the L1g correction level and also the relative calibration using the CCRS-CPF (Canada Centre of Remote Sensing – Calibration Parameter File) coefficient for all images, assuring the same procedures of relative calibration to them. The softwares IDRISI Kilimanjaro (Eastman, 2003), Envi 4.0 (Sulsoft, 2006) and SCORADIS based on the 5S (Zullo Jr., 1994) were used at the processing and analysis of the Landsat 5/TM images. The crop monitoring was performed by means of selected images in order to cover all the soybean crop cycle development periods in the region (emergency, flowering and vegetative peak) in the dates (harvest 2004/2005): november 11, 2004; december 09, 2004; december 25, 2004; january 26, 2005 and february 11, 2005. The transformation or radiometric calibration aims mainly at the reduction of distortions caused by sensor discrepancies. It is performed when there is a need of converting the signal which is captured by the sensor, in target radiance or reflectance, so that the image data can be related to measurements accomplished at the targets on the earth surface. The flowchart on Figure 2 shows these procedures:

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Landsat 5/TM Image

Radiometric

Radiance

Atmospheric Corretion

Apparent BRF

Surface Reflectance

Georeferencing

Apparent BRF

Surface BRF

Radiometric

BRF Normalized

NDVI

GVI

NDVI

GVI

NDVI

GVI

Fig. 2. Flowchart with image processing stages. The first step performed was the conversion of the Digital Number (DN) into radiance values. Next, the radiance data were transformed into Bi-directional Reflectance Factor – BRF apparent (BRFa- top-of-atmosphere). These procedures were accomplished according to Chander and Markham (2003). The conversion of the digital numbers in radiance values was performed by means of post-calibration parameters of the TM sensor, such as minimum radiance ( LMIN λ ) and maximum radiance ( LMAXλ ), listed values for each band of the Landsat 5/TM. The conversion was performed according to Equation 1.

Spectral Characteristics of Soybean during the Vegetative Cycle Using Landsat 5/TM Images in The Western Paraná, Brazil

Lλ = (

LMAXλ − LMIN λ ) × Qcal + LMIN λ Qcal max

459 (1)

Wherein: Lλ : spectral apparent bi-directional radiance in W.(cm2.sr.μm)-1; Qcal : pixel value in digital number (DN); Qcalmax : maximum digital number (DN); Q calmin: minimum digital number (DN); LMAXλ : maximum spectral radiance, in W.(cm2.sr.μm) -1; LMIN λ : minimum spectral radiance, in W.(cm2.sr.μm) -1.

The radiance values were transformed into Bi-directional Reflectance Factor – BRFa by means of the values of the solar zenith angle ( θ s ), distance earth-sun ( d ) and by the average spectral irradiance of the Sun on top-of-atmosphere - ESUN λ (listed), according to Equation 2.

ρ P=

Π.Lλ .d 2 ESUN λ .cosθs

(2)

Wherein: ρ P : BRFa;

π : value/amount 3,141516

Lλ : spectral radiance, in W.(cm2.sr.μm) -1;

d: sun-earth distance, in astronomic units; ESUN λ :mean spectral irradiance from the sun at the top of the atmosphere, W.(cm2.sr.μm)-1;

θ s : solar zenith angle.

The TM sensor images were corrected to the atmosphere effect by using the SCORADIS software. In order to execute the correction algorithm, the following input data was necessary: geographic location (Latitude and Longitude); date and time of the satellite pass; type and quantity of present aerosols; atmospheric model of the gaseous components, mainly water vapor and ozone. The output data is the surface BRF (BRFs). The necessary parameters for the atmospheric correction procedure were aerosols optical thickness, water vapor content (g cm-2 ) and ozone layer thickness (cm atm-1). They were obtained by MODIS images (MOD04_L2: MODIS Level 2 Aerosol over Land and Ocean Product) which has atmospheric parameters. These images were processed on the Envi 4.0 (Sulsoft, 2006) software. The satellite TERRA/MODIS pass time was close to (difference of minutes) the Landsat 5/TM pass, a determinant factor, once the atmospheric conditions have a great variation in time. The atmospheric attributes values were calculated from the arithmetic average of all valid pixels, which were found at the coverage area of the scene 223/077 of the TM sensor (Table 1). After the transformations and corrections performed above, the images were georeferenced and transformed into the latitude and logitude, Datum WGS84 coordinate geographical. The normalization of the images was performed adopting the method proposed by Canty et al. (2004), which must approximate the values of average and variance of each one of the TM image bands in the normalized, average and variance images of the reference image, not discarding, obviously, the spectral evolution, at the time of the original targets of the studied

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Satellite Pass November 23, 2004 December 9, 2004 December 25, 2004 January 26, 2005 February 11, 2005

Optical Thickness of the Aerosols 0.057 0.163 0.086 0.071 0.125

Water vapor (g cm-2)

Ozone (cm atm-1)

3.229 2.802 2.500 3.803 2.233

0.283 0.277 0.272 0.262 0.406

Table 1. Atmospheric parameters taken from the MODIS images image. The image of november 23, 2004 was used as reference to the application of the method on images taken in other dates (target calls), obtaining the normalized BRF (BRFn) values at the end. Supervised classification of the TM images, using the MaxVer algorithm, was carried out to map and estimate areas occupied with soybean throughout the cropping season. At the end of this procedure, the synthesis image was obtained containing the area occupied by the crop. This image was obtained by adding up, by using the boolean operation OU (OR), the result of the classification found in each image, called soybean mask (Mercante et al., 2010). In order to distinguish the spectral response of the soybean crop biomass, in the 36 monitored municipalities, the vegetation indexes of two different approaches were used: one based on bands ratio, which is the NDVI index - Normalized Difference Vegetation Index (Rouse et al., 1973) and another based on spectral transformation, Tasseled Cap (Brightness, Greenness e Wetness) originally proposed by Kauth and Thomas (1976), in which the component “Greenness”, called GVI index - Green Vegetation Index, uses the six non-thermal bands of the Landsat 5/TM or Landsat 7/ETM+ satellites, further assigning different weights for each one of them by linear transformations. The spectral data extraction of the NDVI e GVI in each municipality, of the 36 monitored, was performed by the municipal average of the spectral response contained in the pixels classified as soybean (soybean mask of each studied municipality). Only the pixels which were found completely inside the municipality limits (IBGE, 2001) were computed, and also only those which were not contaminated by clouds (Mercante, 2007). Thus, only the spectral behavior of the NDVI and GVI indexes was obtained, what is typical of the soybean crop for each studied municipality. In the 57 ha area, the spectral data extraction was performed by using a sampling grid, removing the spectral value of the pixel in which the coordinate of the sampled place was located. This was performed in order to gather data of the “pure pixels“, in other words, data that could express only the spectral behavior of the soybean crop (Mercante et al., 2009). A regular sampling grid with 100 points, spaced by 75 meters, was used, as shown in Figure 3. Posteriorly, the sampling points which were found at the surround plot were disconsidered, in other words, those under, at least partial, influence, of other crops or roads that circle the area. Also, the sampling elements contaminated with clouds were eliminated, so there was, for each image data, a variable number of computed pixels. Descriptive statistical analysis of the values of vegetation indexes NDVI and GVI of all images were performed, in order to characterize the different spectral values calculated considering BRFa, BRFs and BRFn. The statistical normality tests Anderson-Darling and T test of average comparison, in the analysis of the results, were accomplished at the level of 5% of significance.

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Fig. 3. Systematic sampling schedule of spectral data in the monitored area.

4. Results and discussions To the characterization of the spectral behavior of soybean in the 57 ha area, expressed in the images of the BRFa, BRFs and BRFn, graphics were created containing the BRF values in each band (wavelength intervals), Figure 4. After achieving the atmospheric correction in all five images, the image of november 23, 2004 was used as reference image in the normalization process. Thus, as this image does not have normalized BRF values, the reason for its choice is that it is totally without interference of clouds and has good visual quality. As it is possible to observe in the graphs (Figure 4), to perform the validation of the atmospheric correction and normalization techniques, comparisons were performed with the spectral behavior typical of the soybean crop. One can see that the atmosphere correction and normalization techniques did not mischaracterize the typical spectral behavior of the soybean crop in the five processed images. The atmospheric correction and normalization by band coherences were verified in each image. It is expected that in the visible bands (bands 1, 2 and 3) there is a decrease in the BRF values, due to molecular scattering, while for the infra-red bands (bands 4,5 and 6) an increase is expected in the BRF according to the correction, mostly in water vapor (Zullo JR., 1994). Figure 4 shows that the method of atmospheric correction and the normalization of images corrected the scattering effects of bands 1, 2 and 3 in all dates, what decrease the BRF value, when they are compared to the apparent values of BRF. A compensation becomes clear in these images, or a gain in BRF in band 4, therefore easing the absorption effect by gases (which decreases the BRF values on this band), according to Vermote et al. (1997). One can see, also, both for the atmospheric correction data and the normalization, a coherence in relation to the behavior of the visible bands – band 1 (blue), band 2 (green) and band 3 (red), over the target, in which predominates the great absorption of energy on the bands 1 and 3, and a light scattering in band 2.

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November 23, 2004

December 25, 2004

December 09, 2004

January 26, 2005

February 11, 2005 Subtitles: BRFa BRFs BRFn B1, B2, B3, B4, B5 e B7: TM / Landsat 5 Bands

Fig. 4. Soybeans spectral response in BRFa, BRFa and BRFn images. As spectral data of the soybean crop were collected from the satellite images during all its cycle, it was possible to evidence the vegetation growth of the crop, mainly proven by the respective BRF of bands 4 (the near infrared strip that responds sensitively to the vegetation strength) in each one of the image dates. One can notice clearly, in the image taken in november 23, 2004, that the BRF in band 4 was approximately 0.30 at the beginning of the crop development, reaching the maximum on january, 26, 2005, with an BRF of approximately 0.6. After this date, the crop possibly enters the senescence period, marked by a decrease in the BRF values in band 4, as was seen in November 02, 2005. The spectral behavior curves of the soybean crop in the images were visually similar between the BRFs and BRFn treatments, in other words, they showed lower values, mainly in band 1, in relation to the BRFa and raised the value of band 4 (Figure 4). However, they showed value differences in other bands for some image dates. That probably occurred due to the ditinct procedures each technique uses in order to ease the influence of atmosphere in the BRF data.

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The soybean mask for the 36 municipalities monitored is shown in Figure 5. The accuracy of the mask was evaluated considering 100 points that were randomly sampled, for each municipality, by using the Global Accuracy metrics and the Kappa Index (Cohen, 1960).

Fig. 5. Soybean mask generated with the TM/Landsat 5 images for the 2004/2005 cropping season The Global Accuracy data varied from 0.65 to 0.95, with an average of 0.87. The Kappa Index data showed an average of 0.82 with variation between 0.63 and 0.92. The Kappa Index values obtained for each municipality are above 0.63, therefore being considered very good according to the classification presented by Landis and Koch (1977). Descriptive statistical analysis and comparative tests of the average NDVI and GVI vegetation indexes were performed for every TM image dates. The results of these analyses for the 36 municipalities are shown on Table 2. The mean comparison (T test) was accomplished among the data obtained from the BRFa, BRFs and BRFn treatments yielding NDVIa, NDVIs, NDVI n, GVIa, GVIs and GVIn according their treatment.

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The results show that in most of the dates, the indexes means were different, except for the images of january 26, 2005 (NDVI BRFa and NDVI BRFn treatments) and november 02, 2005 (for the NDVI and also to the GVI BRFs and BRFn treatments). The CV values were always below 20%, given that, most of time, they were less than 15%, indicating an average homogeneity in the spectral values, according to Gomes‘ classification (2000). Date

November 23, 2004

IV NDVI GVI

NDVI December 9, 2004 GVI

NDVI December 25, 2004 GVI

NDVI January 26, 2005 GVI

NDVI February 11, 2005 GVI

Treatment

Mean

NDVIa(BRFa) NDVIs(BRFs) GVIa(BRFa) GVIs(BRFs) NDVIa(BRFa) NDVIs(BRFs) NDVIn(BRFn) GVIa(BRFa) GVIs(BRFs) GVIn(BRFn) NDVIa( BRFa) NDVIs(BRFs) NDVIn(BRFn) GVIa(BRFa) GVIs(BRFs) GVIn(BRFn) NDVIa(BRFa) NDVIs(BRFs) NDVIn(BRFn) GVIa( BRFa) GVIs( BRFs) GVIn( BRFn) NDVIa(BRFa) NDVIs(BRFs) NDVIn(BRFn) GVIa(BRFa) GVIs(BRFs) GVIn(BRFn)

0.30218 b 0.34626 a 0.05367 b 0.07893 a 0.43835 c 0.48657 b 0.64575 a 0.11930 c 0.15099 b 0.20084 a 0.63303 c 0.67891 b 0.75329 a 0.20843 c 0.23633 b 0.26645 a 0.74656 b 0.79807 a 0.79832 a 0.27990 c 0.33481 a 0.30767 b 0.72212 b 0.78336 a 0.77842 a 0.24313 b 0.29524 a 0.27842 a

Stand. CV (%) Dev. 0.04 13.24 0.04 11.55 0.01 18.63 0.01 12.67 0.07 15.97 0.08 16.44 0.05 7.74 0.03 25.15 0.03 19.87 0.02 9.96 0.04 6.32 0.04 5.89 0.03 3.98 0.02 9.60 0.02 8.46 0.02 7.51 0.04 5.36 0.04 5.01 0.03 3.76 0.03 10.72 0.03 8.96 0.02 6.50 0.05 6.92 0.05 6.38 0.05 6.42 0.03 12.34 0.03 10.16 0.02 7.18

Min

Max

p-value

0.23372 0.26093 0.02958 0.05132 0.33850 0.35959 0.52707 0.08718 0.09931 0.16629 0.55725 0.59490 0.68551 0.17161 0.19784 0.22822 0.66534 0.69274 0.73680 0.22500 0.25751 0.25394 0.58936 0.65685 0.65677 0.18071 0.23238 0.21156

0.38398 0.43336 0.08366 0.11862 0.60306 0.65474 0.76843 0.19314 0.23562 0.26397 0.69559 0.78056 0.80041 0.25521 0.28316 0.32012 0.81439 0.87496 0.85154 0.32444 0.37990 0.34649 0.79675 0.86127 0.85603 0.29108 0.34917 0.32959

0.01 0.05* 0.01 0.05* 0.02 0.03 0.23* 0.01 0.01 0.02 0.07* 0.16* 0.02 0.17* 0.15* 0.24* 0.08* 0.11* 0.33* 0.33* 0.08* 0.12* 0.08* 0.12* 0.05* 0.34* 0.38* 0.39*

- For each Vegetation Index and for each image data, the measurements followed by the same letter do not differ statistically among themselves according to the T test, with a 5% significance level. * with normal probability distribution characteristics according to the Anderson-Darling test with a 5% significance level.

Table 2. Descriptive statistical and comparative tests for the means NDVI and GVI data.

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One can also see that Table 2 did not present the data with normality characteristics and its distribution in the NDVIa and GVIa, in the dates of of november 23, 2004 and december 09, 2004, as well as NDVIs and GVIs , from december 09, 2004, NDVIn from december 25, 2004 and GVIn from december 09, 2004. In all dates, the atmospheric correction (BRFs) and the normalization (BRFn) increased the average values of the vegetation indexes NDVI and GVI in relation to the NDVIa and GVIa. Figures 6 and 7 correspond to the spectral profiles indicated by the temporal variation of the vegetation indexes NDVI and GVI, by using these, in general, it was possible to follow the entire crop development cycle, however, it was not possible to verify the definition of the beginning and end of the cycle, due to the restriction on the number and the image dates during the crop cycle. It was not possible either to verify if the vegetative peak occurred in the image taken on january 10, 2005, by the fact that this image was totally covered by clouds. Thus, it was not possible to observe if the vegetation indexes NDVI and GVI saturated. On the other hand, it was possible to identify the differences in the plantation among the municipalities.

Fig. 6a. Spectral profile NDVIa.

Fig. 6b. Spectral profile NDVIs.

Fig. 6c. Spectral profile NDVIn.

Fig. 6d. Spectral profile of the average NDVIa, NDVIs and NDVIn values for the 36 municipalities.

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Figures 6a, 6b and 6c show the spectral profiles of the soybean crop, represented by the NDVI index of the 36 municipalities in the 2004/2005 cropping season for the BRFa, BRFs and BRFn. It was evidenced that some municipalities have delayed crop development cycles, as shown in the graphs of Figures 6a, 6b and 6c, in which it can be observed that some of the 36 municipalities show higher values in the date of january 26, 2005 and others in the date of february 02, 2005 (delayed cycle). The average values of the 36 municipalities in each date for the NDVIa, NDVIs and NDVIn are shown in Figure 6d. By comparing them, one can observe that the NDVIn values are higher in the dates of december 09, 2004 and december 12, 2004 than the NDVIa and NDVIs. However, in the dates of january 26, 2005 and february 11, 2005, they have higher values than the NDVIa, but they are equal or lower than the NDVIs values. The increase in the NDVIs values in relation to the NDVIa values can be explained by the influence of the atmosphere on bands 3 and 4 (Zullo Jr., 1994). The increase in the NDVIn values in relation to NDVIa and NDVIs occurs due to the use of the base or reference image with the atmosphere correction to perform the normalizing technique. It is supposed that in the image from december 12, 2004, the NDVIn showed higher values than the NDVIs (Figure 6c), probably because the atmosphere influenced less than in the date of november 23, 2004, which was used as reference for the normalization of all the other image dates, increasing the NDVIn value. The greater NDVI average values presented by the spectral profiles were around 0.74 for the NDVIa and 0.79 for the NDVIs and NDVIn in the date of january 26, 2005, provoking a curve mischaracterization, mainly in the beginning of the crop growth, when it is compared to the spectral curve originated from the images corrected by the radiative model. The spectral profiles of the soybean crop growth represented by the GVIa, GVIs and GVIn index for the 36 municipalities are shown in Figures 7a, 7b and 7c. As in the NDVI index, with the GVI it was possible to follow the entire development cycle, noticing differences in the plantation dates among the municipalities. The curves of the average values of the spectral profile of the 36 municipalities for the GVIa, GVIs and GVIn are shown in Figure 7d. The GVI index also presented fairly higher values for the GVIn in relation to the GVIa and GVIs in december 09, 2004 and december 25, 2004. However, it showed higher values for the GVIs in the dates of january 26, 2005 and february 02, 2005 in relation to the GVIn and GVIa. The different values presented among the GVIa, GVIs and GVIn were higher for the normalized images, but they showed minor differences among themselves than those presented in the NDVI curves. The greater average values for the GVI fit around 0.27 for the GVIa, 0.33 for the GVIs and 0.30 for the GVIn, in the date of january 26, 2005. Overall, by using the spectral-temporal profiles, one can observe a potential to follow the soybean development crop growth, opening way to estimate the soybean plantation and harvest dates for any given region or municipality, depending on the number of images and dates. By comparing the spectral-temporal profiles of the NDVI and GVI indexes, one can notice that the NDVI visually presented a higher value amplitude among the municipalities in each one of the image dates, possibly because the NDVI uses only the red and the near infrared bands, and it can be considered as more sensitive to the variations of the development of

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plants (vegetation growth). Thus, the 36 municipalities, for having different crop plantation dates, have variations among them all along the growing stage of the crop, leading to higher amplitudes of the NDVI index values. The NDVI suffered higher impact with the normalization, as shown in Figures 6a, 6b and 6c. The curve had a displacement, showing higher values in the first dates of the cycle.

Fig. 7a. Spectral profile GVIa.

Fig. 7b. Spectral profile GVIs.

Fig. 7c. Spectral profile GVIn.

Fig. 7d. Spectral profile of the average values of vegetation index for GVI a, GVIs and GVIn for the 36 municipalities.

To better understand this behavior, spectral-temporal curves were generated in the monitored 57 ha area, with a known soybean variety, the CD-216, planted on october 16, 2004.

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Figure 8 shows the spectral-temporal profiles for the 57 ha area, according to the NDVIa, NDVIs and NDVIn index. One can verify in the spectral-temporal profiles a gradual increase in the NDVI values of november 23, 2004 until january 26, 2005, when the crop reaches its vegetative peak (highest NDVI value). It is observed that after this date the soybean crop enters the senescence period, marked by loss in the NDVI values. By comparing the three image treatments (apparent, surface and normalized BRF), which originated the NDVI index values, one can notice the same tendency, in other words, a value increase and the index decrease in the senescence stage for the three graphs. However, the NDVIn curve is presented with higher values for the dates of december 09, 2004 and december 25, 2004, markedly changing the spectral curve in relation to the NDVIa and NDVIs values. Figure 8b shows the spectral-temporal profiles for the 57 ha area, according to the GVI index for the apparent, surface and normalized BRF. The marked increase in the GVI values from the date of november 23, 2004 until january 26, 2005, when the crop reaches its vegetative peak in the image, is also verified in the spectral profiles.

Fig. 8a. Spectral-temporal profile in the monitored area, represented by the vegetation index NDVI (BRFa, BRFs and BRFn).

Fig. 8b. Spectral-temporal profile in the monitored area, represented by the vegetation index GVI (BRFa, BRFs and BRFn).

The senescence stage happens next, marked by loss in the GVI index values. In analogy to the NDVI vegetation index, the GVI index showed a more accentuated profile line, both for the vegetative growth of the plant, that occurred until january 26, 2005, and for the senescence period characterized after this date. The three image treatments (apparent, surface and normalized BRF) which originated the GVI indexes once again presented the same tendency. Also, the curve of the GVIn showed higher values on december 09, 2004 and december 25, 2004, changing the form of its spectral curve in relation to the others. One can notice that the crop stops growing after the full formation of the beans, reached in the approximate date of february 02, 2005, behaving according to its characteristics, but the spectral curves of the monitored variety showed that the normalization harmfully affected the spectral behavior of the crop.

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By comparing the spectral curves of the three types of BRF (apparent, surface and normalized), it becomes clear that the normalization technique increased the values of the profiles for all dates, mainly for the dates of december 09, 2004 and december 25, 2004, modifying the spectral curve of this cultivated soybean variety. One can suppose that the normalization an act in a more striking manner, in a negative way, in short cycle crops, and as the target has a more stable profile and a longer cycle, the impact will be minor. El Hajj (2008) offers subsidies in this sense, because he obtained good results among the spectral values calculated by normalization and with those calculated with correction by radiative transfer models for the sugar cane crop.

5. Conclusions and suggestions With the implementation of the atmospheric correction and of the TM/Landsat 5 image normalization technique and, thereafter, with the finding that the obtained results for the surface and normalized BRF were presented as coherent, they become viable for the accomplishment of spectral profile analysis translated by the vegetation indexes (NDVI and GVI). In the elapse of the crop monitoring, it was possible to map the areas with soybean, based on the images from the temporal series of the Landsat 5/TM satellite, for the 2004/2005 cropping season, generating the so called “soybean mask“. However, the methodology used showed some difficult aspects, such as the time demanded for the identification of the soybean areas, by means of the generation of the supervised classification. An alternative to ease these difficulties in similar studies is the use of spectral mixture or Fuzzy logic techniques, which may bring more refined results in the quantification of areas with soybean crop. Despite the obstacles found, the generated soybean mask was very useful in the delimitation of the representative soybean areas as reference to the creation of temporal profiles of the NDVI and GVI indexes of the Landsat 5/TM in the 36 municipalities monitored. By means of the graphs built from the NDVI and GVI spectral profiles, it was possible to follow the soybean crop development cycle. This follow-up was done both in the monitored agriculture areas (57 ha) and in a municipal level, in the 36 monitored municipalities. The spectral behavior of the soybean crop proved to be different for the TM/Landsat 5 images, in the comparison to different treatments BRFa, BRFs and BRFn. The atmospheric correction (BRFs) and the implemented image normalization technique (BRFn) increased the absolute average values of the vegetation indexes in relation to the BRFa (top-of-atmosphere). It was also proven that between these two techniques, atmospheric correction and normalization, the results were statiscally different most of the time. The normalization provoked a negative impact in the spectral-temporal curves, mischaracterizing the spectral behavior of the crop. Perhaps the normalization is less striking for targets having a softer spectral-temporal profile, such as forests or agricultural targets with a longer cycle. The NDVI and GVI spectral average profiles made it possible to follow the soybean crop development cycle. It was also possible to observe that the BRFn mischaracterized the

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soybean crop spectral curve, mainly for the NDVI index among the stage of vegetation growth in the date of 12/09/2004. It is also greatly important to obtain the largest number possible of images for studies involving the temporal follow-up of agricultural crops, from plantation until harvest, diagnosing any occurrence that may harm the crop development. Perhaps, by means of this finding, the importance of the use of sensor data with high temporal resolution was evidenced, because in the event of the TM sensor that was used, there was a limitation of data in a certain period of the crop development by the presence of clouds in the image. Despite the fact that the study has been performed only for one of the regions with the highest production of soybean in Brazil, and with only one crop, one can see, by the obtained results, an objective manner of helping the governmental agencies to gather official, less onerous and more reliable agricultural statistics, that offer a minor data subjectivity.

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Vermote, E.F.; Tanre, D.; Deuze, J.L.; Herman, M.; Morcrette, J.J. (1997). Second simulation of thesatellite signal in the solar spectrum, 6s: an overview. IEEE Transactions on Geoscience andRemote Sensing, 35, 675-686. Zullo Jr, J. (1994). Correção atmosférica de imagens de satélite e aplicações. Thesis (PhD in Electrical Engineering) – Electrical Engineering School, University of Campinas, Campinas, 189p.

21 Bio-Based Nanocomposites Composed of Photo-Cured Soybean-Based Resins and Supramolecular Hydroxystearic Acid Nanofibers Mitsuhiro Shibata Chiba Institute of Technology Japan 1. Introduction Most of crystalline low molecular-weight organic compounds are recrystallized on cooling from hot solution of the organic compounds in appropriate solvents. However, in some cases, the hot solution becomes gelatinous meterial on cooling, which thermo-reversibly goes back to solution on the subsequent heating process, as is shown in Fig.1. The gelation is based on the formation of fibrous network structure over the whole range of the solvent via non-covalent (supramolecular) interactions between the low molecular-weight organic molecules. The structural characteristics of the low molecular-weight gelator are as follows: the presence of multiple functional groups capable of relatively weak physical molecular interactions such as van der Waals forces, hydrogen bonding, electrostatic forces, π-π stacking, and London dispersion forces; asymmetrical, non-planar, and bulky structures which relate to the suppression of crystal packing.

Fig. 1. Gelation and crystallization from the solution of low molecular-weight organic compound.

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Fig. 2. Examples of various low molecular-weight organic gelators. Up to date, a large number of low molecular-weight organic substances, such as (R)-12hydroxystearic acid (HSA) (Tachibana et al., 1979, 1980; Tamura et al., 1994; Rogers & Marangoni, 2009), 1,3;2,4-dibenzylidene-D-sorbitol (Wolfe et al., 1942), and amino acid derivatives (Hanabusa et al., 1999; Hanabusa, 2005; Suzuki et al., 2006) are known to form thermo-reversible gels with supramolecular nanofibrous network in organic solvents (Fig.2). Interest in these compounds originated in the petrochemical industry to immobilize flammable solvents and to aid in the clean up of oil spills (Abdallah & Weiss, 2000; Bhattacharya & Krishnan-Ghosh, 2001). Also, HSA is industrially available organic compound which is derived from castor oil, and is used as a gelator of cooking oils (Dahlke, 1995; Rogers et al., 2008). On the other hand, the use of renewable resources as replacement materials for industrial products is attracting great interest with increasing emphasis on environmental issues, waste disposal, and depletion of earth’s limited petroleum reserves (Kaplan, 1998; Mohanty et al., 2000; Wool, 2005; Yu, 2009). Among the renewable natural resources, triglyceride plant oils represent a major class of such resources and are being used in an increasing number of industrial applications, in addition to being a food sources for human beings (Biermann et al., 2000; Güner et al., 2006; Sharma & Kundu, 2006). Soybean oil is one of the most abundant and inexpensive plant oil. Fig.3 shows the constituent fatty acid units of soybean oil and their approximate content. The major unsaturated fatty acid components of soybean oil are linoleic acid, oleic acid, and linolenic acid. It also composed of saturated fatty acids such as palmitic acid and stearic acid. The average number of C=C double bonds in the triglyceride is ca. 4.6. Unmodified soybean oil can be cationically copolymerized with styrene and divinylbenzene by use of boron trifluoride diethyl etherate to afford thermosetting resins (Li &Larock, 2001). However, the reactivity of the C=C double bonds in soybean oil is not so high that their thermal and photo-curing due to radical polymerization cannot be applied. Soybean oil can be converted to epoxidized soybean oil (ESO) and acrylated epoxidized soybean oil (AESO) with highly reactive functional groups, as is shown in Fig.4. Bio-based epoxy resin, ESO is manufactured by the epoxidation of the double bonds of soybean oil triglycerides with hydrogen peroxide, either in formic acid or in acetic acid, and it is industrially available in large volumes at a reasonable cost (Meffert & Klush 1989; Park et al., 2004; Swern et al., 1945). It is currently mainly used as a plasticizer or stabilizer to

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Fig. 3. Structure and fatty acid components of soybean oil.

Fig. 4. Synthesis of ESO and AESO from soybean oil. modify the properties of plastic resins such as poly(vinyl chloride) and as a reactive modifier or diluent of epoxy resin systems. Several researchers have investigated the curing and conversion of ESO into flexible, semiflexible, and rigid crosslinked resins with various approaches (Gerbase et al., 2002; Miyagawa et al., 2005; Park et al., 2004; Raghavacha, et al., 2000; Rösch & Mülhaupt, 1993; Tamami et al., 2004, Tsujimoto et al., 2003, Warth et al., 1997, Zhu et al., 2004). Bio-based acrylate resin, AESO is prepared by the reaction of ESO and acrylic acid (Khot et al., 2001). It is commercially manufactured in forms such as Ebecryl 860 (UCB Chemicals Company), and has been used extensively in the area of surface coatings. In a similar manner to general acrylate resins, AESO can be free-radically polymerized or copolymerized with reactive diluents such as styrene and methacrylic acid, to give thermosetting resins (Ҫolak & Küsefoğlu, 2007; Lu et al., 2005). It can be also UV-cured by use of a photoinitiator (Ǻkesson et al., 2010). Recently, as environmentally benign reinforcing method of their bio-based polymers, the biocomposites with natural fibers and the nanocomposites with layered silicates have been gathering much attention. Regarding ESO-based composites, the biocomposites with kenaf, kayocell, flax, and hemp etc. have been reported by some groups (Liu et al., 2006; Tran et al., 2006; Warth et al., 1997), and the nanocomposites with organically modified layered silicates have been reported by other groups (Uyama et al., 2003; Uyama et al., 2004; Liu et al., 2005; Song et al., 2006). Also, regarding AESO-based composites, there had been some literatures

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on the biocomposites of a mixture of AESO and styrene with natural fiber mats made of flax, cellulose, pulp and hemp (O′Donell et al., 2004; Khot et al., 2001), and the nanocomposites of AESO and organically modified montmorillonite (Ǻkesson et al., 2010; Lu et al., 2004). If the supramolecular fibrous networks of bio-based low molecular-weight gelator are successfully formed in solid bio-based network polymer matrix, various interesting properties such as complete biodegradability and self repairing etc. are expected for the biobased nanocomposites as compared with conventional network polymer/fiber composites. The present study describes the preparation and properties of the bio-nanocomposites composed of the ESO (Shibata et al, 2009) and AESO crosslinked by the photopolymerization and self-assembled HSA molecules.

2. Experimental part 2.1 Materials Epoxidized soybean oil (ESO, KAPOX S-6, oxirane oxygen 6.7%, iodine value 1.80 g-I2/100 g) was supplied from Kao Corporation (Tokyo, Japan). Acrylated epoxidized soybean oil (AESO) was purchased from Sigma-Aldrich Japan K.K. (Tokyo, Japan). (R)-12Hydroxystearic acid (HSA) was kindly supplied from Itoh Oil Chemicals Co., Ltd. (Yokkaichi, Mie, Japan). As a photoinitiator for cationic polymerization of ESO, 4isobutylphenyl-4’-methylphenyl-iodonium hexafluorophosphate (IRGACURE® 250, Chiba Speciality Chemicals K.K. (Tokyo, Japan)) was used. Photoinitiators for radical polymerization of AESO, 1-hydroxycyclohexyl phenyl ketone (IRGACURE® 184, mp. 4549°C, UV/VIS absorption peaks in methanol:246, 280, 333 nm) and phenyl bis(2,4,6trimethylbenzoyl)phosphine oxide (IRGACURE® 819, mp. 127-133°C UV/VIS absorption peaks in methanol:295, 370 nm) were supplied from Chiba Specialty Chemicals K.K. (Tokyo, Japan). The structure of photo-initiators used in this study is shown in Fig.5.

Fig. 5. Structure of photoinitiators used in this study. 2.2 Preparation of photo-cured ESO/HSA composites A mixture of ESO 10.0 g, HSA 1.0 g, and IRGACURE® 250 0.30 g was heated to 100°C and stirred for 15 min to give a homogeneous liquid. The hot oily substance ca. 2 g was poured

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on a Φ55 mm aluminum pan and was allowed to stand at room temperature to give a gelatinous material. The obtained gel was photo-irradiated under ice cooling for total 15 min at intervals of every 15 s to give a photo-cured ESO/HSA 10/1 film (thickness: ca. 0.8 mm). SPOT-CURE SP-7 (250 W light source, wavelength 240-440 nm, Ushio Inc., Yokohama, Japan) equipped a uniform-radiation optical unit was used for the UV curing (Irradiation distance 200 mm, Irradiation intensity 77.6 mW/cm2). In a similar manner, the photo-cured ESO film without HSA and ESO/HSA 20/1 film were prepared. 2.3 Preparation of photo-cured AESO/HSA composites A solution of IRGACURE® 184 0.030 g and IRGACURE® 819 0.006 g in acetone 2 mL was added to AESO 3.00 g. After the mixture was heated to 130 °C, HSA 0.300 g was added and stirred for 15 min to give a homogeneous liquid. The hot oily substance ca. 2 g was poured on a Φ55 mm aluminum pan and was allowed to stand at room temperature to give a gelatinous material. The obtained gel was photo-irradiated under ice cooling for total 5 min at intervals of every 15 s to give a photo-cured AESO/HSA 10/1 film (thickness: ca. 0.8 mm). SPOT-CURE SP-7 (250 W light source, wavelength 240-440 nm, Ushio Inc., Yokohama, Japan) equipped a uniform-radiation optical unit was used for the UV curing (Irradiation distance 200 mm, Irradiation intensity 77.6 mW/cm2). In a similar manner, the photo-cured AESO film without HSA and AESO/HSA 20/1 film were prepared. 2.4 Measurements Fourier transform infrared (FT-IR) spectra were recorded on a Shimadzu FT-IR 8100 by the attenuated total reflectance (ATR) method. The differential scanning calorimetry (DSC) was performed on a Perkin-Elmer Diamond DSC in a nitrogen atmosphere at a heating rate of 10 ºC/min. The 5% weight loss temperature was measured on a thermogravimetric analyzer TGA-50 (Shimadzu Co. Ltd., Kyoto, Japan) in a nitrogen atmosphere at a heating rate of 20 °C/min. Dynamic mechanical analysis (DMA) of the rectangular films (length 35 mm, width 7 mm, thickness 0.8 mm) was performed on a Rheolograph Solid (Toyo Seiki Co., Ltd, Tokyo, Japan) with a chuck distance of 20 mm, a frequency of 1 Hz and a heating rate of 2 °C/min. Tensile and flexural tests of the rectangular films (length 35 mm, width 5 mm, thickness 0.8 mm) were performed at 20 °C using an Autograph EZ-S (Shimadzu Co. Ltd., Kyoto, Japan). Span length was 20 and 30 mm and the testing speed was 5 and 1 mm/min for tensile and flexural tests, respectively. Five specimens were tested for each set of samples, and the mean values and the standard deviation (σ) were calculated. Transmission electron microscopy (TEM) was performed on a Hitachi H-500 electron microscope with a 75 kV accelerating voltage. The films were sectioned into roughly 120 nm thin sections at –70 °C using an ultramicrotome with a diamond knife and then mounted on 200 mesh copper grids. Polarized and normal optical microscopy was performed on an Olympus BXP microscope equipped with crossed polars, a Sony CCD-IRIS color video camera interfaced to a computer, and a Japan High-tech hot-stage RH-350.

3. Characterization of composites 3.1 Characterization of photo-cured ESO/HSA composites The mixtures of ESO/HSA (10/1 and 20/1) containing a photo-initiator, which were homogeneous liquid at 100 °C became gelatinous materials when cooled to room

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Fig. 6. FT-IR spectra of ESO/HSA(10/1) during the photo-irradiation. temperature. Fig. 6 shows the FT-IR spectra of ESO/HSA(10/1) samples during the photoirradiation. The ESO/HSA before curing showed an absorption peak at 800 cm-1 which is ascribed to anti-symmetrical stretching of epoxy ring, suggesting HSA did not react with ESO during the heating at 100 °C. On the other hand, the absorption peak characteristic to epoxy ring disappeared for the ESO/HSA photo-cured for 5 min, and a broad absorption peak at around 1100 cm-1 which is ascribed to the anti-symmetrical stretching vibration of CO-C formed by the epoxy-ring opening, suggesting that photo-cationic polymerization of ESO smoothly proceeded during the photo-irradiation. The IR spectra of the ESO/HSA samples photo-cured for 10 and 15 min were almost unchanged when compared with that of the sample photo-cured for 5 min. The photo-initiated cationic polymerization is thought to be almost completed by the photo-irradiation for 5 min. Fig. 7 shows the first heating DSC thermograms of ESO and ESO/HSA(10/1) samples containing a photo-initiator before and after the photo-irradiation for 15 min. The ESO before curing showed multiple endothermic peaks between -66 and -9 °C, indicating that ESO is a mixture of several crystalline epoxidized triglycerides. However, the photo-cured ESO had no endothermic peak, suggesting that the crystallization is inhibited by the crosslinking of epoxy groups of ESO. The gelatinous ESO/HSA(10/1) before curing had multiple melting peaks of the ESO component around -63~-3 °C and a thermal transition at 56.7 °C with ΔH = 19.9 J/g-HSA. The temperature and enthalpy change of the thermal transition were considerably lower than Tm 77.7 °C and ΔHm 159 J/g-HSA of crystalline HSA, indicating that the thermal change is not due to melting of crystalline but due to isotropization of the mesophase composed of nano-fibrillar HSA aggregates. The reason

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Fig. 7. The first heating DSC thermograms of HSA and the ESO and ESO/HSA(10/1) samples containing a photo-initiator before and after the photo-irradiation for 15 min. Reprinted from Shibata et al. (2009) with permission of John Wiley & Sons Inc. why the enthalpy change is much smaller should be attributed to the fact that the mesophase is thermodynamically unstable when compared to the perfect crystal. Also, in the case of nanofibers with tiny dimensions, the free surface energy plays an important role and decreases the measured enthalpy change of the nano-fibrillar aggregates. The photocured ESO/HSA(10/1) showed only a broad transition around 67 °C (ΔH =22.6 J/g-HSA). The fact that the enthalpy change does not decrease compared with the value before curing indicates that the supramolecular aggregation of HSA does not collapse. The broadening of the transition curve and shift to a higher temperature region suggest that the HSA supramolecular aggregates are restricted by the ESO crosslinked structure and changes to a more aggregated structure. In addition, the disappearance of the endothermic peak around -70~0 °C suggests the occurrence of the crosslinking reaction of ESO component in a similar manner to the photo-cured ESO. Fig. 8 shows TEM images of the ESO/HSA(10/1) cured for 15 min. In the photograph of a lower magnification, dendritic aggregates of HSA were appeared in the cured ESO matrix. In the photograph of a higher magnification, it is obvious that the aggregates are composed of numerous nanofibers. It was confirmed by the DSC and TEM observations that the photo-cured ESO/HSA is a bio-based nanocomposite.

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Fig. 8. TEM images of the ESO/HSA(10/1) cured for 15 min:(a) lower magnification and (b) higher magnification. Reprinted from Shibata et al. (2009) with permission of John Wiley & Sons Inc. 3.2 Characterization of photo-cured AESO/HSA composites The mixtures of AESO/HSA (10/1 and 20/1) containing photo-initiators, which were homogeneous liquid at 130 °C became gelatinous materials when cooled to room temperature. Fig.9 shows FT-IR spectra of AESO/HSA(20/1) and AESO/HSA(10/1) photocured for 5 and 10 min. The FT-IR absorption peaks at 1408 and 810 cm-1 observed for AESO, which were assigned to olefinic C-H group in-plane and out-of-plane bending, almost disappeared for the photo-cured AESO/HSA, indicating that photo-polymerization of AESO smoothly proceeded. Fig. 10 shows the first heating DSC thermograms of AESO/HSA(10/1) samples containing photo-initiators before and after the photo-irradiation for 5 min and 10 min. The gelatinous AESO/HSA(10/1) before curing had a thermal transition at 56.6 °C with ΔH = 63.8 J/gHSA. The temperature and enthalpy change of the thermal transition were considerably lower than Tm 77.7 °C and ΔHm 159 J/g-HSA of crystalline HSA, indicating that the thermal change is not due to melting of crystalline but due to isotropization of the mesophase

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Fig. 9. FT-IR spectra of AESO and the AESO/HSA(20/1) and AESO/HSA(10/1) photocured for 5 and 10 min.

Fig. 10. The first heating DSC thermograms of AESO/HSA(10/1) containing photo-initiators before and after the photo-irradiation for 5 and 10 min.

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Fig. 11. Polarized and normal optical photomicrographs of HSA, SBO/HSA(10/1) gel.

Fig. 12. Polarized and normal optical photomicrographs of AESO/HSA(10/1) gel and AESO/HSA(10/1) photo-cured for 5 min.

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composed of nano-fibrillar HSA aggregates. The AESO/HSA(10/1) photo-cured for 5 min showed an endothermic peak at 77.8 °C which is higher than that before photo-curing and is almost the same as Tm of crystalline HSA. This result suggests that supramolecularly aggregated HSA molecules densely packed and changed to HSA crystalline aggregates during the photo-polymerization of AESO. Fig.11 shows polarized and normal optical photomicrographs of HSA, soybean oil (SBO)/HSA(10/1) gel. The HSA started to crystallize at around 75 °C from the melt, and had a crystalline texture under polarized light. The formed crystals were not fibrous materials, as are obvious from the normal optical micrograph. A typical organogel of SBO/HSA10 displayed birefringence to some extent under polarized light and fibrous aggregates were formed, as are shown in the normal optical micrograph. Fig.12 shows polarized and normal optical photo-micrographs of AESO/HSA(10/1) gel and AESO/HSA(10/1) photo-cured for 5 min. In case of AESO/HSA(10/1) gel, it is obvious that fibrous networks are homogeneously distributed in the liquid AESO. The diameter of the

Fig. 13. TEM images of the AESO/HSA samples cured for 5 min: (a) lower magnification of AESO/HSA(20/1), (b) higher magnification of AESO/HSA(20/1) , (c) lower magnification of AESO/HSA(10/1), and (d) higher magnification of AESO/HSA(10/1).

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fibrous aggregates was micro- to submicro-meters, which can be identified by optical microscope. However, most of fibrous networks disappeared for the photo-cured AESO/HSA(10/1), and some aggregated crystals was observed, indicating that the supramolecular networks changed to crystalline phase during the photo-curing of AESO. Fig. 13 shows TEM images of the AESO/HSA(20/1) and AESO/HSA(10/1) cured for 5 min. In the photographs of a lower magnification, it is obvious that HSA crystalline aggregates are certainly formed for both the composites. It is shown from the photographs of a higher magnification that the HSA crystalline aggregates forms a network structure for AESO/HSA(20/1) and they densely packed for AESO/HSA(10/1). The fact that photo-cured AESO/HSA has heterogeneous crystalline HSA aggregates is marked contrast to the supramolecular nano-fibrous networks of photo-cured ESO/HSA. The difference may arise from the following possibilities: the cross-linked AESO has a lower affinity with HSA than the cross-linked ESO does, and HSA molecules precipitated out from the AESO/HSA network structure.

4. Properties of composites 4.1 Mechanical properties of photo-cured ESO/HSA composites Fig. 14 shows DMA charts of the ESO, ESO/HSA(20/1 and 10/1) photo-cured for 15 min. The tan peak temperature corresponding to glass transition temperature (Tg: 8.9 °C for ESO, 9.0 °C for ESO/HSA20/1, 10.4 °C for ESO/HSA10/1) little increased with increasing HSA content. In general, the addition of miscible low molecular weight compound to polymer causes a decrease of Tg due to a plasticizing effect. However, in this case, the Tg did not decrease in spite of the addition of low-molecular weight HSA. This result indicates that HSA is scarcely dissolved in the crosslinked ESO matrix, and most of HSA forms the mesophase composed of supramolecular aggregates. The storage modulus (E’) at around 20~60 °C increased with increasing HSA content. The E’ decreased over 60 °C due to the isotropization of the HSA nanofiber aggregates. The DMA results confirmed the nanofiberreinforcement effect of HSA.

Fig. 14. DMA charts of the ESO, ESO/HSA(20/1 and 10/1) photo-cured for 15 min. Reprinted from Shibata et al. (2009) with permission of John Wiley & Sons Inc.

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Fig. 15. Tensile properties of the photo-cured ESO/HSA nanocomposites Fig. 15 shows the tensile properties of the photo-cured ESO/HSA nanocomposites. Tensile strength and modulus increased with increasing HSA content in agreement with the result of DMA. Although elongation at break generally did not improve in case of plant-based fiber reinforced plastics, the photo-cured ESO/HSA nanocomposites showed a little higher elongation at break than the photo-cured ESO. It may be attributed to the reasons that the supramolecular nanofiber is less rigid than plant-based fiber, and that interfacial adhesion is expected to be good because both the matrix polymer and fiber are plant oil-based organic compounds. 4.2 Mechanical properties of photo-cured AESO/HSA composites Fig. 16 shows DMA charts of the AESO and AESO/HSA(20/1 and 10/1) photo-cured for 5 min. The tan peak temperature corresponding to glass transition temperature (Tg: 45.1 °C for AESO, 39.2 °C for AESO/HSA20/1, 38.1 °C for AESO/HSA10/1) decreased a little with increasing HSA content. This trend is a marked contrast with the trend of the photo-cured ESO/HSA. This result indicates that HSA is somewhat dissolved in the crosslinked AESO matrix, and most of HSA forms the crystalline phase which does not affect the molecular motion of the cross-linked AESO. The storage modulus (E’) at around 70~140 °C decreased with increasing HSA content. The decrease of E’ around 70 °C for photo-cured AESO/HSA(10/1) is due to the melting of the densely packed crystalline phase of HSA. A similar decrease of E’ around 70 °C was not observed for photo-cured AESO/HSA(20/1),

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suggesting that the melting of small amount of heterogeneously distributed HSA crystals does not affect the modulus of the whole material. Fig. 17 shows the flexural properties of the photo-cured AESO/HSA composites. Flexural strength and modulus decreased with increasing HSA content in agreement with the result of DMA. This result indicates that the HSA crystalline phase which does not homogeneously distribute in the matrix resin does not reinforce the material. The formation of supramolecular HSA aggregates which are homogeneously distributed in the matrix is very important to get reinforced materials.

Fig. 16. DMA charts of the AESO and AESO/HSA(20/1 and 10/1) photo-cured for 5 min.

Fig. 17. Flexural properties of the photo-cured AESO/HSA composites. 4.3 Thermodegradable properties of composites Table 1 summarizes 5 % weight loss temperature measured by TGA for the photo-cured ESO/HSA and AESO/HSA composites. The 5 % weight loss temperature of HSA itself was 276 °C, which is considerably lower than those of the photo-cured ESO and AESO. Therefore, the 5 % weight loss temperature of photo-cured ESO/HSA and AESO/HSA decreased with increasing HSA content.

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5 % weight loss temperature (°C) 374.8 360.1 344.4 369.2 320.5 303.8

Table 1. The temperature at 5 % weight loss measured by TGA for the photo-cured samples.

5. Application of this technology This technology using cross-linked polymer can be applied to thermoplastic polymer. Recently, we reported the biocomposites of castor-oil modified poly( -caprolactone) (COPCL) and self-assembled HSA fibers (Shibata et al., 2010). The CO-PCL is synthesized by the ring-opening polymerization of -caprolactone (CL) initiated from the hydroxyl groups of castor oil in a feed molar ratio of CL/OH = 30/1, as is shown in Fig. 18. The degree of polymerization of -caprolactone unit per one segment of CO-PCL measured by 1H-NMR was 29.7. After the CO-PCL was mixed with HSA at 100 °C, the mixture was gradually cooled to room temperature to give a CO-PCL/HSA composite. The abbreviation COPCL/HSA5, 10, and 15 means the HSA content 5, 10, and 15 phr. In order to confirm the formation of organogel during the cooling stage of CO-PCL/HSA samples, the rheological measurement was performed. When the CO-PCL/HSA samples were cooled from 80 °C, the elastic modulus (G’) of liquefied sample rapidly increased around 67-55 °C and again increased around 47-43 °C (Fig. 19). The first increase of G’ is due to an increase of viscosity due to the formation of HSA organogel. The temperature at which the G’ starts to rise increased with increasing content of HSA. The second increase of G’ is due to the crystallization of CO-PCL. In case of CO-PCL itself, the G’ rapidly rose at 39 °C due to the crystallization, indicating that the formation of HSA organogel promotes the crystallization of CO-PCL component. This is supported by the comparison of the crystallization temperature (Tc) determined from the cooling DSC thermograms of COPCL/HSA and CO-PCL. Thus, the Tc’s of CO-PCL/HSA5, 10, and 15 samples are 32.1, 33.4, and 33.5 °C, respectively, which are higher than that of CO-PCL (15.8 °C). Fig. 20 shows polarized and normal optical photomicrographs of CO-PCL/HSA10 during the cooling stage from 85 °C at a rate of -1 °C/min and the subsequent heating stage from 40 °C at a rate of 1°C/min. When the CO-PCL/HSA10 was cooled from 85 °C, at which the sample is isotropic liquid, fibrous network started to form at around 60 °C, and then crystallization of CO-PCL occurred. We could not clearly see the fibrous network in the normal optical photograph of 40 °C, because of the presence of CO-PCL crystals. However, when the sample was again heated at a rate of 1 °C/min from 40 °C, fibrous network could be seen at around 55 °C which is near the Tm of CO-PCL. This result shows that the fibrous network which was formed during the cooling process is not collapsed by the crystallization of CO-PCL. Fig.21 shows the first heating DSC thermograms of the CO-PCL, CO-PCL/HSA samples annealed at 60 °C for 2 h. Melting temperature of the annealed CO-PCL was 55.2 °C, which was lower than that of as-prepared CO-PCL (60.1 °C). Since as-prepared CO-PCL was precipitated from the chloroform solution by dilution with methanol, the sample had a higher melting temperature than the annealed sample which was crystallized from the melt.

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Fig. 18. Synthesis of CO-PCL. Reprinted from Shibata et al. (2010) with permission of John Wiley & Sons Inc.

Fig. 19. Temperature dependency of G’ at a cooling rate of -1 °C /min for CO-PCL, COPCL/HSA5, CO-PCL/HSA10, and CO-PCL/HSA15. Reprinted from Shibata et al. (2010) with permission of John Wiley & Sons Inc.

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Fig. 20. Polarized and normal optical photomicrographs of CO-PCL/HSA10 samples during the cooling stage from 85 °C at a rate of -1 °C/min and the subsequent heating stage from 40 °C at a rate of 1 °C/min. Reprinted from Shibata et al. (2010) with permission of John Wiley & Sons Inc.

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Fig. 21. The first heating DSC thermograms of the CO-PCL, CO-PCL/HSA5, COPCL/HSA10, and CO-PCL/HSA15 samples annealed at 60°C for 2 h. Reprinted from Shibata et al. (2010) with permission of John Wiley & Sons Inc.

Fig. 22. Flexural properties at 25 °C for the CO-PCL/HSA samples annealed at 60 °C for 2 h (●) and without annealing (○). Reprinted from Shibata et al. (2010) with permission of John Wiley & Sons Inc.

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The annealed CO-PCL/HSA5 showed a weak endothermic peak (Ti = 66.4 °C, ΔHi = 22.3 J/g-HSA) in addition to a strong endothermic peak due to the melting of CO-PCL component (Tm = 57.2°C, ΔHm = 55.9 J/g-HSA). The temperature of the weak peak is near the Ti of SBO/HSA10 (66.2 °C), suggesting the formation of supramolecular aggregates of HSA. The Ti and ΔHi of CO-PCL/HSA samples increased with increasing HSA amount, suggesting the HSA supramolecular mesophase is more densely packed and gradually becomes close to crystalline phase. Fig.22 shows flexural strength and modulus at 20°C for the CO-PCL/HSA samples annealed at 60 °C for 2 h and without annealing. For both the samples, the flexural modulus tended to increase with increasing HSA content in the range of 0-10 phr and leveled off at 15 phr. Also, the annealed CO-PCL/HSA5 and CO-PCL/HSA10 had higher flexural modulus than the corresponding samples without annealing. However, for the CO-PCL/HSA15, the effect of annealing on the increase of modulus was little. This result is in agreement with the fact that the CO-PCL/HSA15 without annealing had rather higher ΔHi than the annealed COPCL/HSA15.

6. Conclusions Bio-based nanocomposites of photo-cured ESO/HSA and AESO/HSA were prepared, and their morphologies and properties were investigated. The DSC analysis revealed that Ti of the mesogenic HSA aggregates for the photo-cured ESO/HSA is 67 °C, which is higher than that (56.7 °C) for the ESO/HSA before curing and lower than Tm (77.7°C) of crystalline HSA. The TEM observation of the photo-cured ESO/HSA revealed that dendritic clusters of HSA nanofibers are formed in the crosslinked ESO matrix. Storage modulus, tensile modulus, and tensile strength of the photo-cured ESO/HSA increased with increasing HSA content. In contrast to the ESO/HSA, the fibrous network of AESO/HSA gel changed to crystalline phase during the photo-curing, as is obvious from the change of Ti in the DSC thermograms and microscopic analyses. In case of the photo-cured AESO/HSA, the flexural strength and modulus decreased with increasing HSA content. This result is attributed to the fact that the formed HSA crystalline phase heterogeneously distributed in the cross-linked matrix AESO resin. The difference arises from the fact that the cross-linked AESO has a lower affinity with HSA than the cross-linked ESO does. From these observations, it was revealed that the selection of soybean-based resin with appropriate affinity to HSA is important to get a highperformance biocomposite with supramolecular HSA nanofibers. The method established in the crosslinked ESO/HSA was applied to the bio-based thermoplastic resin, CO-PCL. As a result of the preparation of CO-PCL/HSA biocomposites, fibrous network was successfully formed in the CO-PCL matrix. The formation of fibrous network was confirmed by the normal and polarized optical microscopic analyses. The flexural and storage modulus of the CO-PCL/HSA samples increased with increasing HSA content. The CO-PCL/HSA is a new molecular composite whose mechanical recycling and self repairing is possible due to the repeated melting and self assembling of the fibrous network. The use of renewable triglyceride plant oil as replacement materials of petroleum-based industrial materials is very important in order to save earth’s limited petroleum resources and suppress global warming and environmental issues due to waste disposal. All the components (ESO, AESO, CO-PCL, and HSA) of the composites developed in this study are based on plant

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oil resources. In general, plant oil-based materials have relatively poor mechanical properties because the plant oil contains long-chain fatty acid moiety. The drawback was successfully improved by the formation of supramolecular HSA fibrous network. It is expected that the biocomposites in this study are completely biodegradable materials.

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Lu, J.; Hong, C. K. & Wool, R. P. (2004). Bio-based nanocomposites from functionalized plant oils and layered silicate. Journal of Polymer Science, Part B: Polymer Physics, Vol. 42, No. 8, pp. 1441-1450, ISSN: 0887-6266. Lu, J.; Khot, S. & Wool, R. P. (2005). New sheet molding compound resins from soybean oil. I. Synthesis and characterization. Polymer, Vol. 46, No. 1, pp. 71-80, ISSN: 0032-3861. Meffert, A. & Kluth, H. (1989). Process for the preparation of modified triglycerides. U.S. Patent, 4,886, 893. Miyagawa, H.; Misra, M.; Drzal, L.& Mohanty, A. K. (2005). Fracture toughness and impact strength of anhydride-cured biobased epoxy. Polymer Engineering & Science, Vol. 45, No. 4, pp. 487-495, ISSN: 0032-3888. Mohanty, A. K.; Misra, M. & Hinrichsen, G. (2000). Biofibres, biodegradable polymers and biocomposites: An overview. Macromolecular Materials & Engineering, Vol. 276/277, pp. 1-24, ISSN: 1438-7492. O’Donnell, A.; Dweib, M. A. & Wool, R. P. (2004). Natural fiber composites with plant oilbased resin. Composites Scienece and Technology, Vol. 64, No. 9, pp. 1135-1145, ISSN: 0266-3538. Park, S.-J.; Jin, F.-L. & Lee, J.-R. (2004). Synthesis and thermal properties of epoxidized vegetable oil. Macromolular Rapid Communications, Vol. 25, No. 6, pp. 724-727, ISSN: 1022-1336. Raghavachar, R.; Sarnecki, G.; Baghdachi, J. & Massingill, J. (2000). Cationic, thermally cured coatings using epoxidized soybean oil. Journal of Coatings Technology, Vol. 72, No. 909, pp. 125-133, ISSN: 0361-8773. Rogers, M. A. & Marangoni, A. G. (2009). Solvent-modulated nucleation and crystallization kinetics of 12-hydroxystearic acid: A nonisothermal approach. Langmuir, Vol. 25, No. 15, pp. 8556-8566, ISSN:0743-7463. Rogers, M. A.; Wright, A. & Marangoni, A. G. (2008). Crystalline stability of self-assembled fibrillar networks of 12-hydroxystearic acid in edible oils. Food Research International, Vol. 41, No. 10, pp. 1026-1034, ISSN:0963-9969. Rösch, J. & Mülhaupt, R. (1993). Polymers from renewable resoureces: polyester resins and blends based upon anhydride-cured epoxidized soybean oil. Polymer Bulletin, Vol. 31, No. 6, pp. 679-685, ISSN: 0170-0839. Sharma, V. & Kundu, P. P. (2006). Addition polymers from natural oils―A review. Progress in Polymer Science, Vol. 31, pp. 983-1008, ISSN: 0079-6700. Shibata, M.; Teramoto, N. & Kaneko, K. (2010). Molecular composites composed of castor oil-modified poly(e-caprolactone) and self-assembled hydroxystearic acid fibers. Jouranl of Polymer Science, Part B: Polymer Physics, Vol 48, No.12, pp. 1281-1289, ISSN:0887-6266. Shibata, M.; Teramoto, N.; Someya, Y. & Suzuki, S. (2009). Bio-based nanocomposites composed of photo-cured epoxidized soybean oil and supramolecular hydroxystearic acid nanofibers. , Jouranl of Polymer Science, Part B: Polymer Physics, Vol 47, No.7, pp. 669-673, ISSN:0887-6266. Song, B.; Chen, W.; Liu, Z. & Erhan, S. (2006). Compressive properties of epoxidized soybean oil/clay nanocomposites. International Journal of Plasticity, Vol. 22, No. 8, pp.1549-1568, ISSN: 0749-6419.

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Swern, D.; Billen, G. N.; Findley, T. W. & Scanlan, J. T. (1945). Hydroxylation of Monounsaturated Fatty Materials with Hydrogen Peroxide. Journal of the American Chemical Society, Vol. 67, No. 10, pp. 1786-1789, ISSN: 0002-7863. Suzuki, M.; Sato, T.; Shirai, H. & Hanabusa, K. (2006). Powerful low-molecular-weight gelators based on L-valine and L-isoleicine with various terminal groups. New Journal of Chemistry, Vol. 30, pp. 1184-1191, ISSN:1144-0546. Tachibana, T.; Mori, T. & Hori, K. (1979). New type of twisted mesophase in jellies and solid films of chiral 12-hydroxyoctadecanoic acid. Nature, Vol. 278, pp. 578-579, ISSN: 0028-0836. Tachibana, T.; Mori, T. & Hori, K. (1980). Chiral mesophases of 12-hydroxyoctadecanoic acid in jelly and in the solid state. I. A new type of lyotropic mesophase in jelly with organic solvents. Bulletin of the Chemical Society of Japan, Vol. 53, No. 6, pp. 17141719, ISSN: 0009-2673. Tamami, B.; Sohn, S. & Wilkes, G. L. (2004). Incorporation of carbon dioxide into soybean oil and subsequent preparation and studies of nonisocyanate polyurethane networks. Journal of Applied Polymer Science, Vol. 92. No. 2. pp. 883-891, ISSN: 0021-8995. Tamura, T.; Suetake, T.; Ohkubo, T.& Ohbu, K. (1994). Effect of alkali metal ions on gel formation in the 12-hydroxystearic acid/soybean oil system. Journal of the American Oil Chemists’ Society, Vol. 71, No. 8, pp. 857-861, ISSN: 0003-021X. Tsujimoto, T.; Uyama, H. & Kobayashi, S. (2003). Green nanocomposites from renewable resources: Biodegradable plant oil-silica hybrid coatings. Macromolular Rapid Communications, Vol. 24, No. 12, pp. 711-714, ISSN: 1022-1336. Tran, P.; Graiver, D. & Narayan, R. (2006). Biocomposites synthesized from chemically modified soy oil and biofibers. Journal of Applied Polymer Science, Vol. 102, No. 1, pp. 69-75, ISSN: 0021-8995. Uyama, H.; Kuwabara, M.; Tsujimoto, T.; Nakano, M.; Usuki, A. & Kobayashi, S. (2003). Green nanocomposites from renewable resources: plant oil-clay hybrid materials. Chemistry of Materials, Vol. 15, No. 13, pp.2492-2494, ISSN: 0897-4756. Uyama, H.; Kuwabara, M.; Tsujimoto, T.; Nakano, M.; Usuki, A. & Kobayashi, S. (2004). Organic-inorganic hybrids from renewable plant oils and clay. Macromolecular Bioscience, Vol. 4, No. 3, pp.354-360, ISSN: 0897-4756. Warth, H.; Mülhaupt, R.; Hoffmann, B.; Lawson, S. (1997). Polyester networks based upon epoxidized and maleinated natural oils. Angewandte Makromol eculare Chemie, Vol. 249, No. , pp. 79-92, ISSN: 0003-3146. Wolfe, J. K.; Hann, R. M. & Hudson, C. S. (1942). 1,2,3,4-Dibenzylidene-D-sorbitol. Journal of the American Chemical Society, Vol. 64, No. 7, pp. 1493-1497, ISSN: 0002-7863. Wool, R. P. (2005). Polymers and Composite Resins From Plant Oils, In: Bio-Based Polymers and Composites, Wool, R. P. & Sun, X. S. (ed.), pp. 56-111, Elsevier Academic Press, ISBN: 0-12-763952-7, Burlington. Yu, L. (Ed.) (2009). Biodegradable Polymer Blends and Composites from Renewable Resources. John Wiley & Sons, ISBN: 98-0-470-14683-5, Hoboken. Zhu, J.; Chandrashekhara, K.; Flanigan, V. & Kapila, S. (2004). Curing and mechanical characterization of a soy-based epoxy resin system. Journal of Applied Polymer Science, Vol. 91. No. 6, pp. 3513-3518, ISSN: 0021-8995.

22 Transgenic Residues in Soybean-based Foods Mónica L. Chávez-González, Carolina Flores-Gallegos, Víctor M. García-Lazalde, Cristóbal Noé Aguilar and Raúl Rodríguez-Herrera Food Research Department. School of Chemistry. Universidad Autónoma de Coahuila México 1. Introduction The technology of genetically modified foods (also called GM foods) allows selection of an specific genetic trait from one organism and introduces it in the genetic code of the food source through genetic engineering techniques. This has made possible to develop food crops with specific beneficial traits and elimination of undesirable traits in others. In spite of the great agricultural advantages of transgenic crops these do not have acceptance in some countries because: a) the suspicious of consumer as result of allergic reactions observed with some transgenic crops, b) the lack of worldwide regulations to these crops, and c) the negative side effects to environment by the massive farming of transgenic crops, for example loss of genetic diversity, creation of higher adapting weeds, the migration of transgenic genes to their wild relatives and less likely migration of transgenic genes to other unrelated organisms by horizontal transference. Also, the contamination of food with transgenic residues has persuaded different countries to restrict importation of food made with transgenic plants or labelling food or ingredients as or from transgenic crops. There are worrying aspects about the effect of transgenic food over human health such as: 1) many of transgenic genes used today have never been present in human diet, making impossible to know the effect that they will cause, 2) the allergic potential of some proteins codified by some transgenic genes, and 3) in the genetic engineering of crops, many times a gene with antibiotic resistance is included like selectable gene, having the possibility that this gene migrates to pathogen bacteria that affect the health of plants, human and animal, developing bacterial resistance to those antibiotics making hard to have bacterial control. In the present work, literature is reviewed in order to present a perspective about some of the above concerns. In addition, are presented results of different studies in order to answer the following questions: a). Do DNA and proteins are degraded during the traditional soybean food processing?, b). Does food processing affect the persistence of transgenic residues? and c) Does is possible to develop more efficient techniques for detection of transgenic residues in soybean-based foods?

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2. Transgenic seeds in the world market The global food demand has increased the need for improved crops. At 2006 the biotechnological industry reached several goals, for example, the annual area planted with transgenic crops exceeded for first time 100 million hectares; the number of farmers whom cultivate biotechnological crops exceeded 10 million (10.3), where it is important to stand out that 90 % of these farmers were of scanty resources in developing countries whom increased their income across this crops helping to relieve this way their poverty; the accumulated area from 1996 to 2006 exceeded the half trillion of hectares, with an increase without precedent of 60 times between 1996 and 2006, doing this agricultural technology the most rapidly adopted in the recent history (James, 2006). Inside transgenic crops and food that now exist on the market, four are the most important: maize, cotton, soybean and canola. These transgenic products are recent on the world market; since 1996 they were planted freely in The United States of America (USA). In USA during 2006, 54.46 million of hectares of transgenic crops were sown, principally soybean, maize, cotton, canola, gourd, papaya and alfalfa, followed by Argentina, 18.0 million (soybean, maize and cotton), Brazil with 11.5 million, Canada with 6.1 million, India with 3.8 million of transgenic cotton, China with 3.5 million, Paraguay with 2.0 million ha of transgenic soybean, and SouthAfrica with 1.4 million of hectares of transgenic maize, soybean and cotton. There are a group of countries that sown less than 100 000 hectares of transgenic crops such as: Colombia, France, Iran, Portugal and Honduras, where the predominant crop is maize, and one more group of countries like Uruguay, The Philippines, Australia, Romania, Mexico and Spain where less than one million hectares are cultivated (Massieu-Trigo, 2009). At the present time the most common genetic transformations in these transgenic crops are basically two: tolerance to herbicides and resistance to insects. In the USA, the area planted with transgenic soybean was estimated as 77.5 million ha during 2009, 2 % more with regard to 2008. Compared with 2008 there was an increase in the surface planted by 200, 000 ha or more in Kansas, Mississippi, Missouri, North Dakota and South Dakota. The major decrease happened in Nebraska, up to 400, 000 ha with regard to 2008. The record of the major planted surface was estimated for Kansas, New York, North Dakota and Pennsylvania (NASS, 2009). Although big advances have been achieved in the last years for transgenic crops adoption, it still persists distrust over of these products, which has limited the adoption of these technologies by many small farmers, mainly for five reasons: • The investments in transgenic crops by the private sector • The public expenditure on these technologies • Limited access to patent technologies • Constant worries on food safety and risks to environment • Weak capacity of regulation in developing countries In addition to the previous limitations, a great controversy persists in rich countries especially the European Union where the principal points in this controversy are the safety of environment, food health and social risks of genetically modified organisms (GMO); nevertheless this situation must not conceal the potential of these products to contribute in poverty reduction among farmers from developing countries, where GMO can play an important role in the small scale agricultural systems and provide nourishing food for the poor consumers in developing countries (Ragasa & Pehu, 2008).

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3. Transgenic crops in Mexico Genetically modified products are on top of an agro-political debate, mainly as a result of concerns based on the consumers food safety issues such as the result of eating a GMO (Ash et al., 2003). Mexico is one of the countries that most import grain seeds, and permissions to plant transgenic crops have been released by official agencies. For example, in the state of Coahuila, transgenic cotton have been planted since 1997; in the state of Chiapas has been planted transgenic soybean and transgenic tomato with delayed ripening characteristics has been sown in Guasave, Sinaloa, among others (Castro-Soto & Zinn, 2001; Macias-de la Cerda, 2006). In 1989, a National Agricultural Biosafety Committee was created in Mexico as an institution to approve the release of genetically modified organisms and products to environment and Mexican market, and also establish policies and regulations on GMOs. Ten years later the government of Mexico created the Commission on Biosafety and Genetically Modified Organisms (CIBIOGEM) in order to coordinate policies on biosecurity, production, import, export, mobilization, propagation, liberation, consumption, use and development of GMOs products and by-products (Cantu-Iris, 2006). In Mexico, many GM products are consumed without being aware of it. Corn and soybeans are being used as ingredients for food products (vegetable oils, bread, flour, milk, ice cream, etc.). Soybean is present in more than 60% of these products (Castro-Soto & Zinn, 2001). GM crops in Mexico during the period from 1996 to 2009 were grown in 16 states. Among the companies that have obtained permits for planting GM crops in this state are Excerpts Agro, Monsanto and IARC. In Sinaloa, tomato, zucchini, corn, melon, cotton, chile and soybean have been cultivated. Among the companies that have obtained permits for planting GM crops in this state are: Campbells, Sinalopasta, Calgene, Asgrow Mexicana, Monsanto, Harris Moran of Mexico, DNA Plant Technology, Peto Seed, Pioneer and Rhone Poulenc. In the State of Guanajuato were grown potatoes, tomato, squash, corn, rice and wheat. Among the companies and institutions that have obtained permits for planting of GM crops in this state are: CINVESTAV, UpJhon Asgrow, ISK BioSec, Seminis Vegetable Seeds, PetoSeeds, Asgrow and the National Autonomous University of Mexico (UNAM). While in Veracruz have been planted, cotton and soybeans. Among the companies that have obtained permits for planting of GM crops in this state include: CIBA-GEIGY and Monsanto. In Baja California have been cultivated tomatos, cotton, pepper, squash, melon, canola and flax. Among the companies that have obtained permits for planting of GM crops in this state are: PetroSA Mexican Agritope, Aventis CropScience, Seminis Vegetable Seeds, Monsanto, DNA Plant Technology, and SVS Mexicana Calgery. In the State of Mexico have been planted wheat, corn, alfalfa and carnation. Among the companies and institutions that have obtained permits for planting of GM crops in this state include: CIMMYT, Florigene CEFINI-UNAM and Europe BV. In other states, like Tamaulipas, cotton and soybeans have been planted. Among the companies that have obtained permits for planting of GM crops are: Malvinas, Monsanto, Rhone Poulenc and Avenis Cropscience. In Baja California Sur have been grown tomato, zucchini, tomatoes, melons, corn and cotton. The companies that have obtained permits for planting GM crops in this state include: Agritope, Asgrow Mexicana, Seminis Vegetable Seeds, Pioneer, SVS Mexicana, DNA Plant Technology and Monsanto. While in Morelos has been planted maize by CIMMYT, in Jalisco have been sown soybean, potato, tomato, pepper, maize, and also have been tested genetically modified microorganisms. Among the companies and institutions that have obtained permits for the planting of GM crops in this state are: Hybrid Seeds SA de CV, CINVESTAV, CIBA-GEIGY

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Mexicana, Technology and Asgrow Plant DNA. In Coahuila was grown cotton and soybeans by Monsanto Company and CINVESTAV. In Sonora have been planted canola, potato, tomato, cotton, corn, soybean and melon. Among the companies that have obtained permits for the planting of GM crops in this state include: Calgene, CINVESTAV, DNA Plant Technology, Harris Moran, Monsanto, Mycogen Mexicana and Rhone Poulenc. In Nuevo Leon, squash was planted by PetoSeeds and Seminis Vegetable Seeds. In San Luis Potosi were planted cotton and soybeans by Monsanto. In Chihuahua was reported the planting of cotton by Monsanto, while in Nayarit were planted soybeans, maize and tomato by Pioneer, Monsanto, Zeneca and Asgrow. In 2006, the area planted with soybean in Mexico was 0.1 million ha from which 0.005 million hectares were GM soybean. Soybean production in Mexico is not enough to satisfy domestic demand hence the import of this leguminous is a necessity, mainly from USA, Argentina and Brazil (Cruz-Flores, 2005). Nowadays, Mexican regulation does not require labeling of products produced from or with genetically modified raw materials. This situation promotes public debate because some scientifics state that the primary reason for avoiding genetic modification in food is the great uncertainty that exists around these products, bringing with it consequences, for example, that consumers neither have the right to choose which product to consume nor strict control of diseases caused by these products, so as to generate a complaint against the companies if they generate harmful effects on the health of consumers (Castro-Soto & Zinn, 2001). The labeling of GM food products has become a major battleground between the biotechnology industry and society (Cruz-Flores, 2005).

4. Risks associated with the transgenic seeds The use of GMO´s must be done based on a rigorous analysis of the risks that they could represent for environment, biodiversity and human health. Though commonly risks related to socioeconomic and cultural activities are not evaluated explicitly, these also can play an important role in making decisions. It is difficult to identify all adverse effects that can happen. Nevertheless, it turns out to be an illustrative exercise to identify and to measure some of the attributes that can be associated with possible adverse effects, in the different levels of biological complexity. Modified crops can present consequences in the survival, reproduction, competitive capacity or interactions with other organisms. The most serious ecological risks that present the commercial use of transgenic crops are: the expansion of the transgenic crops threatens the genetic diversity for the simplification of the systems of crops and the promotion of the genetic erosion (Rissler & Mellon, 1996; Krimsky & Wrubel, 1996). The potential transfer of genes from crops resistant to herbicides (CRHs) to wild varieties or semi-domesticated relatives could make the control of these wild relatives more difficult or reduce the biodiversity of these plant species. Recombination of vectors which could generate varieties of virus more harmful, especially in GM plants designed for viral resistance on the basis of genes viral, and that the plagues of insects will develop rapidly resistance to the transgenic crops. The history has showed that a large area cultivated with an alone crop is very vulnerable to new pathogenic or plague, the widespread use of an alone crop leads to the loss of genetic diversity (Robinson, 1996). There exist evidences that do not leave any doubt that cultivation of large areas with few modified varieties has been an important reason of the genetic

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erosion, when the governmental massive campaigns encourage the farmers to adopt modern varieties pushing them to leave a lot of local varieties (Tripp, 1996). The uniformity caused by the increase of the area of crop with a number of smallest varieties is a source of risk for the farmers, when the modern varieties are more vulnerable to diseases and to the assault of plagues (Robinson, 1996).When an alone herbicide is used repeatedly on a crop, the opportunities of which resistance develops to the herbicide in the population of weeds increases (Holt et al., 1993). Cassia obtusifolia, an aggressive weed in the soybean and the maize cropping in the Southeast of the USA has exhibited resistance to the herbicide imidazolinone (Goldburg, 1992). Some risks involved in the consumption of GM soybean have been reported. Marshall (2007) indicated the relation of a diet based on resistant soybean to glyphosate and low possibilities of survival at birth. Also, there have been associated allergic reactions to the consumption of modified soybean (Burks et al., 1988; Ogawa et al., 1993; Burks & Fuchs, 1995). Nevertheless Batiste et al. in 2005, reported a study where volunteers did not showed allergic reaction after consumption of modified soybean and modified maize. Nevertheless, the benefits of the modified soybean exceed in much to the adverse effects.

5. Benefits of the transgenic soybean Traditionally soybean based-foods of have been consumed for centuries in most of the Asian countries and recently, this food has had a great popularity in the west hemisphere (Messina, 2008), this popularity is because reports on soybean bioactives components which may have effects on disease prevention as well as in the treatment of some diseases. Soybean products contain bioactives phytochemicals such as isoflavones, saponins, phytic acids, phytosterols, trypsin inhibitors and peptides (Isanga & Zhang, 2008). The isoflavones are classified like phytoestrogens and have been postulated for being natural alternatives for the hormonal therapy for woman menopause (Messina, 2008). Some investigations have involved the phytochemicals contained in soybean as functional in the reduction of cholesterol and prevention of: cardiovascular diseases, diabetic symptoms, bone lose straight and cancer (Isanga & Zhang, 2008). Regarding cardiovascular diseases, it has been suggested that soybean-based food can help to reduce the levels of cholesterol; soybean will help to this change since provide quality protein, in addition is low in saturated fat and is devoid of cholesterol (Messina & Lane, 2007). Some studies reports that transgenic soybean with high content of α-tocoferol by expressing the gene γ-tocoferol metiltransferase of Perilla frutescens (Tayva et al., 2007), could have a role on prevention of lipids oxidative damage during seed storage and germination, besides an increase of α-tocoferol content might have a potential to increase vitamin E consumption in diet. Nevertheless some bioactives compounds in soybean are brought for possessing some adverse effects to human health (Isanga & Zhang, 2008). Besides the benefits of soybean on health it is important to stand out the importance of genetically modified soybean as a crop. In transgenic soybean the principal characteristics incorporated are: resistance to insects and herbicide tolerance which are based on the cry and epsps (synthetase 5-enolpyruvylshikimate-3-phospahte) genes, respectively (Rincon et al., 1999). Because this type of modifications, soybean can resist to adverse circumstances, which contribute to improve of the earnings. This type of technologies was adopted since 1996 in USA, where soybean tolerant to glyphosate was introduced; this soybean had a great adoption by farmers (Dill et al., 2008). Glyphosate known commercially as Roundup ® acts

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in all vegetable species, inhibiting the enzymes essential for synthesis of aromatic amino acids. These amino acids are necessary for photosynthesis. When these amino acids are not able, plant die or stop their growth. This GM soybean could tolerate glyphosate applications; this tolerance is conferred by a bacterial enzyme (Malik et al., 1989; Barry et al., 1992). The employment of this type of transgenic crops represents one of the technologies of more quickly adoption for weed control (Dill et al., 2008). There has been demonstrated that the technology of modified organisms increases the total productivity of factors in 10 %, provoking cost saving, reduction in the use of toxic herbicides and involve positive repercussions in environment (Qaim & Traxler, 2005).

6. Most common transgenic sequences incorporates to soybean Weed control is a critical step in soybean cropping. For effective control of weeds, farmers commonly selected herbicides, based on several factors: cost, environmental risks, weeds and crop damage. Advances in plant biotechnology have made possible to insert genes into soybean conferring specific tolerance to herbicides and providing benefits to weed control in soybean fields. Glyphosate-tolerant soybeans may impact general agronomic practices on soybean, offering to farmers a new option for broad spectrum weed control. The development of crops tolerant to glyphosate has been pursued since the early 1980, the mechanism of change in site was used for soybeans, where a protein target insensitive to the herbicide was identified and introduced into the crop through genetic engineering techniques (Padgett et al., 1995). With similar techniques were generated soybean varieties that produce high amounts of fatty acids and oleic acid generated by the transfer of a second copy of the gene for soybean-fatty-acid desaturase (GmFad2-1) by biolistic transformation of soybean. In addition, soybean varieties have been transformed in order to develop disease and insect resistance. The control of plant pathogens is a major challenge for the agricultural industry, because they are responsible for large economic losses annually. 6.1 Herbicide tolerant soybean The specificity of glyphosate blocking the activity of 5-enol-Pyruvyl-Shikimate-3-Phosphate Synthase (EPSP synthase), which catalyzes the reaction of shikimate -3-phosphate (S3P) and phosphoenolpyruvate (PEP) of 5-enol-pyruvyl-shikimate-3-Phosphate (EPSP) and phosphate. Glyphosate inhibition of EPSPS prevents the plant to produce an aromatic amino acid essential to protein synthesis and some secondary metabolites (Padgett et al., 1995). The EPSPS enzyme is present in all plants, bacteria and fungi, but not in animals, which do not produce their own aromatic amino acids. In plants, EPSPS is localized in chloroplasts or plasmids, in the treatment of glyphosate, genetically modified crops remains unaffected due to the constant action of the enzyme EPSPS tolerant meets the needs of aromatic amino acids of the plant. On the other hand, the bar gene confers tolerance to glyphosate ammonium, and this was cloned from a common soil actinomycete Streptomyces hygroscopicus and encodes for the phosphinotricin N acetylase (PAT) enzyme that detoxifies the phosphinotricin enzyme by acetylation in an inactive compound. This kind of herbicide-resistant soybeans comes with a reporter gene that encodes for the enzyme beta-D-glucuronidase (GUS) used development to select the transformed plants during tissue crop regeneration and multiplication. Genetic modification was made by the biolistic technique, using transcriptome-derived sequences of virus 35S cauliflower mosaic (CaMV), (GM Crop Database ACS-GM003-1, ACS-GM001-8,

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ACS-GM002-9 GM-005 ACS-3, GM-006-ACS 4, 2010). Other crops engineered for tolerance to herbicides are those that express a protein AtHASL altered (which is structurally similar to AtHASL native, except for the replacement of asparagine at position 653) encoded by the gene csr1-2 of Arabidopsis thaliana, which confers tolerance to imidazolinone (GM Crop Database BPS-CV127-9). 6.2 Fatty acid content modifications The soybean with high oleic oil production contains high levels of oleic acid greater than that found in olive and canola oil. This kind of soybean was achieved using the endogenous gene GmFad2-1. This genetic modification affects seeds and other plant parts like leaves. These changes result in more oil, more stable to heat with enhanced nutritional and functional properties. Other features transferred with the GmFad2-1 gene and the uidA (a colorimetric marker used for selection of transformed plant lines during the processing of soybeans) that encodes for beta-glucuronidase enzyme, derived from the bacterium Escherichia coli. And the bla gene (a marker used for selection of transformed bacteria from non transformed during the cloning of recombinant DNA and steps that are performed in the laboratory prior to the transformation of plant cells) that encodes the enzyme beta lactamase that confers resistance to some beta-lactam antibiotics such as penicillin and ampicillin (GM Crop Database DD 026005-3, 2010). Pioneer used a technique of microprojectile bombardment for secondary embryos co-transforming plant cells with two purified DNA fragments, the PHP19340A (fragment of 2924 bp) containing the Gm-Fad2 gene-1 (includes the promoter for the Kunitz trypsin inhibitor gene (KTi3)) and the PHP17752A gene (fragment of 4512 bp) containing the Gm-Hra gene and gene fragments inserted and microsomal omega-6-desaturase that results in the endogenous expression of this enzyme, with the intention of produce soybeans containing monounsaturated fatty acids (oleic oil) (GM Crop Database, DP-305423-1, 2010). 6.3 Crops resistant to viral and fungal resistance To obtain transgenic plants resistant to viral diseases it has been used pathogen-derived genes, introduced into the plant. Viral genes have been transferred to crops to limit the ability of invaders to replicate within plant cells, this strategy induces resistance to viral diseases and opens new possibilities for the effective control of these diseases. Besides, it has been developed plant varieties resistant to fungal attack, by inserting genes of resistance, once the resistant plant, and the genes persist in future generations through conventional breeding procedures (Macias-de la Cerda, 2006). 6.4 Insect-resistant crops Most of the inserted genes into plants for insect resistance have been isolated from Bacillus thuringiensis, a soil bacterium whose spores contain a crystalline protein (cry) which when ingested by an insect decomposes and releases a toxin called endotoxin delta. The toxin is embedded in the walls of insect gut creating pores, resulting in ionic imbalance and paralysis to the insect digestive system. It has been identified versions of these genes effective against different orders of insects (Cantu-Iris, 2006). Bth toxins are not toxic either to humans or environment, each strain produces proteins that have a specific effect on certain species of insects, usually within the same order (Table 1) (Macias-de la Cerda, 2006).

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Gene

Order of susceptible insects

cryIA(a), cryIA(b), cryIA(c)

Lepidoptera

cry1B, cry1C, cry1C

Lepidoptera

cry II

Lepidoptera, dipteran

cry III

Coleoptera

cry IV

Dipteran

cry IV

Lepidoptera, coleptera

Table 1. Specificity of the toxins encoded by different Bt genes

7. Transgenic residues in soybean-based foods The main features introduced to transgenic crops are: resistance to different species of insect pests, and tolerance to certain herbicides, all this is achieved by introducing to soybean plants one or more genes that confer to these plants that characteristics. In 2002, a study was initiated to determine if a). Do DNA and proteins are degraded during the traditional soybean-based food processing?, b). Does soybean food processing affect the persistence of transgenic residues? and c). Does is possible to develop more efficient techniques for detection of transgenic residues in soybean-based food? 7.1 Do DNA and proteins are degraded during traditional soybean food processing? In the Universidad Autonoma de Coahuila, 22 lots of soybean imported to Mexico from different countries were analyzed by PCR. PCR was carried out using specific primers for accompanying transgenic sequences (ubi,35S, ntpII, bar, gus, luc, us, ocs) and 9 transgenic events (cry1ab, cry1e, cry2A, cry3A, cry11A, epsps, als, ec, accasa). Results showed that all the analyzed samples presented at least one transgenic sequence. The most frequently (90%) detected promoter in the imported soybean was ubi. The marker most detected in the samples was bar in 75% of the samples, while the reporter most detected was luc in 45% and the terminators ocs and nos were detected in 45% of the samples. On the other hand, the most detected transgenic event was epsps in 100% of the cases. The results suggest that the great majority of soybean lots that Mexico imports are mixtures of soybean genetically modified varieties (Macias-de la Cerda, 2006 and Cantu-Iris, 2006). The importance of analyzing grain shipments imported into a determined country is because is important to determine the level of contamination of these grains with GM crops, the potential risk for the native biodiversity and pollution of native grain production (Padgett et al., 1995; Cantu-Iris, 2006). With the above mentioned soybean lots were elaborated different traditional soybean-based foods and critical steps of food processing (drastic changes in temperature and pH) were identified. DNA was extracted from the samples of each critical step. Segments of epsps, cry A1 genes and CaMV promoter were amplified by PCR from tofu, milk, yogurt, sausage, flour and soybean sprouted. This work identified the presence of the promoter in 45% of the samples and the gene cry1A in 35% of the samples. This suggested that processing conditions of traditional soybean-based foods did not affect the transgenic segment persistence (Gonzalez, 2004).

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7.2 Does food processing affect the persistence of transgenic residues Commercial food products from major cities of Coahuila, Mexico were screened to identify residues of transgenic DNA and/or proteins. After performed, an inventory on all products that contained a soybean-based ingredient in a commercial grocery store in the city of Saltillo, Coahuila, Mexico, two hundred and forty five food products were identified and grouped in 15 classes according to the soybean ingredient as well as the manufacturing process used for their elaboration. Similar sampling was made for the different food classes in the cities of Monclova, Piedras Negras and Torreon. Segments of the transgenic genes epsps, cry 1A and the CaMV promoter were amplified using PCR. The transgenic proteins 5enolpyruvylshikimate-3-phosphate synthase (CP4 EPSPS) and an insecticidal crystal protein (Cry 1Ab/Ac) were identified using DAS-ELISA (Double antibody sandwich - Enzymatic Linked Immunoassay Analysis). Presence of transgenic genes and/or proteins was identified in 35.3% of the commercial products samples (Cruz-Flores, 2005). It was observed the presence of transgenic residues in foods with different levels of processing (Table 2 and 3). Table 2 presents the list of the 15 different classes of commercial products based on soybean ingredients; Table 3 presents a list of products were the presence of cry and epsps genes and CP4-EPSPS and CRY 1 AB/1Ac proteins were detected (Cruz-Flores, 2005). 7.3 Is it possible to develop more efficient techniques for the detection of transgenic residues in soybean-based foods? Macias-de la Cerda (2006) reported the development and optimization of a multiplex-PCR protocol for the simultaneous detection of three accompanying transgenic sequences. Multiplex PCR was performed with DNA from soybean plants for the detection of transgenic sequences, allowing the amplification of nos (125 bp), ntpII (271bp) and luc (450 bp) at an annealing primers temperature of 62ºC. In this case a multiplex PCR technique based on amplification of the accompanying transgenic sequences could be a very valuable tool for detecting DNA transgenic residues. On the other hand Cantu-Iris (2006) reported the optimization of a multiplex PCR for detection of two or more transgenic sequences. The primers sequence was evaluated in order to avoid the primer dimmer formation. Eighteen primers were evaluated with the software FastPCR® and only three primer interactions were observed, which must be avoided for multiplex PCR. The simultaneous amplification of three of the most common transgenic events (cry 1Ab, epsps and als) in a same reaction was obtained with a optimized multiplex PCR. These results could have an impact on a more efficient (speed and cost) detection of genetically modified organisms. 7.3.1 Corroborating the efficiency of multiplex PCR for detecting accompanying sequences and transgenic events in commercial food Hernandez (2007) reported detection of simultaneously different transgenic residues from DNA of home-making and distributed commercially soybean-based foods using the multiplex PCR technique. Pairs of specific primers for the events epsps, cry 1Ab/1As, and als and for the accompanying sequences nos, luc and ntp II were used. The soybean samples identified as transgenic were used to elaborate three soybean-based foods (milk, sausages and tofu). In addition, 20 commercial foods with at least one soybean ingredient were used for DNA extraction. It was possible to detect simultaneously three transgenic events (epsps, als and cry 1 A/b) and three accompanying transgenic sequences (epsps, als and cry 1 A/b) from DNA isolated from commercial and homemade soybean-based foods.

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Soybean class

Typical product

Soybean oil

Soybean oil

Product Characteristics Soybean 100% Mixture of oils from soybean and other plants. Vegetal oil with soybean as additive

Vegetal oil

Vegetal oil

Others oil with lecithin

Vegetal oil

Products with soybean oil and without heat treatment

Salad seasoning, mayonnaise

No use of temperature for food processing.

cereal, bread, flour, cookies chocolate, cheese, French potatoes

Use of temperature for food processing.

Soybean lecithin without heat treatment

Frozen dessert

Soybean lecithin as emulsifier /anti oxidant and no use of temperature for food processing.

Soybean lecithin with heat treatment

Chocolate, candies, bread, soup, cookies

Soybean lecithin as emulsifier /anti oxidant and use of temperature for food processing.

Hydrolyzed soybean protein/soybean sauce without heat treatment

Sauces, seasoning.

No use of temperature for food processing and protein/sauce is used as flavoring

Hydrolyzed soybean protein/soybean sauce with heat treatment

Chocolates, cereals, soup, peanut, chips

Use of temperature for food processing and protein/sauce is used as flavoring

Soybean flour

Bread, flour

Soybean flour as an ingredient.

Isolated soybean protein

Cereal, bread, sausage, meta

Containing more than 90% of soybean proteins.

Soybean oil with heat treatment

Textured soybean

Imitation of bacon, tuna, sausage

Fibrillated proteins imitating beef tissue texture. Soybean is only mentioned in the Soybean without specifications Cereal, chips, beverages product label. Active enzymes

Tortilla

Soybean concentrate

Sausage

Containing about 70% proteins from soybean.

Table 2. Fifteen different classes of commercial products based on soybean-ingredients evaluated by Cruz-Flores (2005)

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PRODUCT

EPSPS

35S

cry1A

Hot cakes flour 1 Hot cakes flour 2 Salad seasoning Salad cookies Chocolate bar Imitation of bacon Sausage Tortillas Powder for drinks Oat cereal Powder chocolate Flour for chocolate cake Turkey sausage Peanut snacks Seasoning with chesse Bread Cookies Instantaneous soup Bread(M) Salad cookies (M) Cookies (M) Turkey sausage (M) Peanut snacks (T) Hot cakes flour (T) Dust chocolate (T) Salad cookies (P) Peanut snacks (P) Bread (P) Instantaneous soup (P) Hot cakes flour (P)

+ + + + + + +

+ + + + + + + + + + + + + + + + + + + + +

+ + + + + + + + + + -

Protein CP4 EPSPS + + + + + + + + + + + + + + + + + +

Protein Cry 1Ab/1Ac -

Table 3. Identification of cry genes, EPSPS CP4-EPSPS protein and cry1AB/1AC in some commercial food products based on soybean (Cruz-Flores, 2005).

8. Detection of transgenic residues in soybean-based food In modern agriculture, soybean is considered one of the most profitable crops due to nutritional properties. The soybean contents up to 20 % of high quality oil for human consumption. This makes soybean a product of great importance in manufacturing food products such as milk, food supplements, vegetables and other derivatives, and mixtures of beef with soybean, reducing significantly costs and improving nutritional quality. In the world, this type of soybean occupied 60% of the planted area of biotech crops (Germini et al., 2004b).In recent years, genetically modified organisms (GMOs) have

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attracted great interest and many policy debates about its impact on environment and the safety of genetically modified plants and foods (Germini et al., 2004b). The identification of GMOs has become a topic of great interest as the number of products derived from GMOs that have been released to the market has increased, also has increased demands by consumers for strict regulations on the labeling of these products (Feriotto et al., 2002). The European Union (EU) has regulations since 1997 for labeling products containing more than 0.9% Roundup Ready soybean (Reg. 49/200/EEC), as well as additives and flavorings from GM crops (Reg. 50/2000/EEC) (Germini et al., 2004a). In Mexico, on March 18, 2005 the Law on Biosafety of Genetically Modified Organisms (DOF 18/03/2005) was published in the Federation official journal. The scientific community's interest in the development of efficient methodologies for the detection of GMOs is related to the fact that they agree on the need for highly automated detection systems for identification of GMO-free food as well as quantitative tests to verify levels of GMOs in food (Feriotto et al., 2002). Two techniques have been developed mainly for the detection of GMOs: DNA analysis by PCR and protein analysis (Germini et al., 2004b). 8.1 Techniques based on DNA identification 8.1.1 Polymerase chain reaction (PCR) This technique is an enzymatic method that allows copying a particular area of a genome, being able to get up to thousand copies in a test tube. PCR uses DNA polymerase which is able to copy DNA molecules. The technique requires a known nucleotide sequence of a desired gene region, since the PCR needs short oligonucleotides or primers, complementary to sequences present in the gene or genes of interest from which the DNA polymerase will incorporate nucleotides complementary to the chain being copied (Germini et al., 2004b). Vőllenhofer et al. (1999) designed primers to amplify parts of the 35S promoter, NOS terminator and marker NTPII. PCR / hybridization protocols were established for detection of Roundup Ready soybeans. In addition to hybridization, confirmation of results was possible using a restriction analysis. The aim of this work was to select pairs of primers that allow the restriction enzyme digestion of PCR products using inexpensive enzymes to reduce costs of analysis. None of the amplified fragments was greater than 200 bp, so the analysis of DNA from highly processed foods was not a problem. This procedure was highly sensitive and specific for detection of GMOs and provides a useful tool in the analysis of raw materials and processed foods. Jeng et al. (2006) analyzed soybean seeds and their products for detecting Roundup Ready soybean traces. Most soybean products were generated using a heating process, which damages the DNA used for PCR reaction. However, amplification was achieved for the detection of transgenic soybean using epsps, lectin and 35S genes. Fermentation is another important part of the process of soybean-based products, where the DNA after a long period of fermentation (over 180 days) is severely damaged. That’s why the epsps gene was not identified in these samples. 8.1.2 Multiplex PCR Many of the PCR products for GMOs detection involve reactions that amplify a single target sequence. Multiplex PCR is a variation of the conventional technique in which two or more target sequences are simultaneously amplified in the same reaction. This method has high reliability, flexibility and reduced costs (James et al., 2003). Some studies have described the use of multiplex PCR as a rapid and convenient assay for the detection of GMOs.

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Permingeat et al. (2002) developed a multiplex PCR method for simultaneous detection of GMO in soybeans. Using only two pairs of primers from each cry1A(b) and pat genes, multiple reaction was optimized by combining the four primers at different concentrations. For the detection of transgenic soybean, multiplex PCR was developed for accurate and simultaneous detection of epsps and nos fragments, both corresponding to the introduced gene in Roundup Ready soybean, using two primer pairs, one for the 35S-epsps gene and the other for nos. Rudi et al. (2003) designed a multiplex PCR-based assay for quantification of DNA. This method is based on two stages of PCR; in the first few cycles, bipartite primers containing the termination 5 'universal sequence and the 3' region of a specific sequence were used for each of the events that were analyzed genetically. The unused primers were then degraded with an exonuclease specific to single-stranded DNA. The second stage was performed containing only PCR primers complementary to the universal sequence of the 5 'region. The removal of the primers is essential for quantitative PCR. Oligonucleotides hybridized to internal fragments of the PCR products were then marked for specific sequences. The hybridization of labeled oligonucleotides to their complementary sequences in a DNA array enabled multiple detection, where quantitative information is obtained in the range of 0.1 to 2% for the different GMO analyzed. In this study, 17 different food and seed samples were examined using a 20-plex system for simultaneous detection of seven different GM maize lines (Bt176, Bt11, MON810, T25, GA21, and DBT418 CBH351). Germini et al. (2004a) proposed a method based on multiplex PCR for simultaneous detection of four types of GM maize (MON810, Bt11, Bt176 and GA21), one for soybeans (Roundup Ready) and two controls (for maize zein gene and the gene lectin for soybeans), on seeds, raw materials and processed foods. For this work, it was necessary to use seven pairs of primers in the same reaction, noting that the length of amplified segment was an important factor to detection of transgenic DNA in processed foods due to false negatives for DNA degradation in food processing. For this reason, primers amplifying segments with lengths no larger than 270 bp were designed for the detection of GMOs even in highly processed products. James et al.(2003) designed a multiplex PCR protocol to detect the 9 most common GM crops for soybeans (Roundup Ready), maize (event 176, Bt11, MON810, T14/25) and canola (GT73, HCN92/28, MS8/RF3, Oxy 235). Primer combinations were used allowing the identification of specific lines. In an identification system, simultaneous amplification was used rather than specific target detection for the identification of four GM maize lines. The non-specific amplification was used as a reliable tool for the identification of a GM maize line. The initiator cry1A 4-3 '(antisense) recognizes two sites in the DNA extracted from transgenic corn event 176 resulting in the amplification of 152 bp (expected) and 485 bp (not expected) products. The last fragment was sequenced and confirmed that it corresponded to the gene cry1A (b). The simultaneous amplification has the ability to identify specific lines of GM, as well as identifying new GM lines containing similar transgenes. 8.1.3 Real-time PCR (RTi-PCR) According to European regulations, the quantification is essential for labeling GMOs and satisfying these regulations. There are many research programs for the development of reliable methods, specific, standardized and quantitative detection of GMOs in food and seeds (Hernandez et al. 2004). The use of real-time PCR is a practical and quick as a

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quantitative method to detect GMOs in processed food samples (Ding et al., 2004, Hernandez et al. 2004). This PCR technique has made possible to quantify the initial amount of nucleic acids during PCR reaction without the necessity of further analysis (Mason et al., 2002). In addition, the amount of amplified DNA can be measured by fluorescence detection at the process, because the fluorescence emission produced in the reaction is proportional to the amount of DNA formed. This allows to know and record at all times the kinetics of the amplification reaction. The fluorescence detection systems used in real-time PCR can be of two types: specific intercalating agents and probes labeled with fluorophores (Costa, 2004). Vaïtilingom et al. (1999) developed a quantitative method for detection of transgenic maize events Maximazer 176 and Roundup Ready soybeans. The use of the detection system ABI Prism 7700 allowed the determination of the amplified product accumulation through a fluorescent probe (TaqMan). The use of Dutp and UNG in the PCR reaction eliminates contamination, which is the major source of false positives. Rott et al. (2004) evaluated two essays, one of conventional PCR to detect the presence of RR soybean. The second was realtime PCR to quantify the amount of RR soybean present in samples that were positive in the first essay. In quantitative tests, two groups were showed: soybean foods with traces of RR (≤ 0.4%) suggesting a contamination of RR soybeans and soybean foods with high levels of RR soy. 8.1.4 Capillary gel electrophoresis with laser-induced fluorescence With the increase of GMOs that have been developed for food applications, the ability to detect different transgenic sequences in a single reaction has become an important characteristic of any detection method (Garcia-Cañas et al., 2002b). However, there is a demand for new analytical methods which help to provide new and reliable information for the characterization of genetically modified foods (Garcia-Cañas et al., 2002a). The use of PCR in combination with capillary gel electrophoresis (CGE) seems a good alternative for the detection of GMOs in food, based on DNA analysis. In combination with competitive PCR, the CGE analysis allows accurate detection and amplification of different transgenes, as an alternative to real-time PCR. However, the CGE UV detection has low sensitivity and usually cannot be applied to samples with concentrations below 10-6 M. The use of laser-induced fluorescence (LIF) in CGE dramatically increases the detection limit and linear dynamic range compared with that obtained with UV (Garcia-Cañas et al., 2002b). This technique involves a high degree of automation, use minimal amounts of samples and reagents, is capable of producing separations of PCR products with high efficiency, and has proven to be a good alternative to obtain accurate and sensitive quantification of DNA fragments amplified by PCR (Garcia-Cañas et al., 2004a). Garcia-Cañas et al. (2002a), using capillary gel electrophoresis detected the presence of transgenic corn flour. The method is based on the extraction and PCR amplification of specific fragments of transgenic maize and subsequent analysis by capillary gel electrophoresis with UV detection and laser-induced fluorescence. A comparison between the two protocols for the detection of DNA based on UV absorption and laser-induced fluorescence was done, yielding a more sensitive detector with laser-induced. A sample of corn flour showed contamination not detectable by UV, which would generate a false positive. Additionally, Garcia-Cañas et al., (2002b) compared four different fluorescent intercalating agents for the detection of transgenic corn flour using capillary gel electrophoresis with laser-induced fluorescence. Fluorescent intercalating agents compared

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were: YOPRP-1, SYBR-GREEN-1, ethidium bromide and EnhanCE. It was shown that SYBRGREEN-I and Yopro-1 gave better detection limits than EnhanCE and ethidium bromide. Also, Garcia-Cañas et al. (2004b) devised a method for the simultaneous detection of five transgenic maize events using multiplex PCR followed by capillary electrophoresis laserinduced fluorescence. This method used Hexaplex PCR protocol for amplification of GM maize varieties Bt11, T25, MON810, GA21 and Bt176, demonstrating that the use of gel electrophoresis laser-induced fluorescence is very useful and informative for PCR optimization of multiple parameters such as time extension, the concentration of PCR buffer and primers. The method developed is very sensitive and also solves the problem of false positives. 8.1.5 Microarrays The microarray technology can increase the ease and speed of analysis of PCR products. DNA microarray is an analytical system that allows simultaneous detection of many nucleic acid sequences in a sample. Each DNA sequence is represented by a covalent bond of an oligonucleotide probe on the modified surface of a piece of glass. The probes in the array are hybridized with fluorescent marker PCR products. Scanner analysis reveals the presence of labeled material containing the complementary sequences of those marked in the microarray (Germini et al., 2004b). 8.1.6 Peptidic Nucleic Acids (PNAs) Peptide nucleic acids (PNAs) are analogues of oligonucleicos where the sugar-phosphate has been replaced by a pseudopeptide chain of the monomer N-aminoethylglycine (Germini et al., 2004b). Unlike nucleic acids, PNAs do not contain pentose phosphate groups. The main advantage of these biomimetic molecules compared to their natural analogues is their high affinity to liaise with DNA strands. The lack of electrostatic repulsion between them makes these links stronger than those between two strands of DNA (Gonzalez et al., 2005). Germini (2004b) reported the combination of microarray technology with PNAs for the detection of genetically modified soybean. Several PNAs were designed, synthesized and attached covalently to functional areas to build a microarray and to identify the constitutive gene lectin and the epsps gene. The effect of PNA length on signal intensity and specificity was assessed, as well as the conditions for the detection of PCR products of both single and double stranded. The best results were obtained with single-stranded PCR products and with long PNAs. The advantages of this method over other technologies include: PNAs are more efficient and hybrids are more stable than oligonucleotides, the PNAs are highly specific sequence and molecular microarrays allow simultaneous analysis of many sequences. 8.1.7 Surface Plasmon Resonance (SPR) This method is based on biosensors capable of playing a biospecific interaction analysis (BIA) to monitor a variety of molecular reactions in real time. It is an optical technique that detects and quantifies changes in refractive index in the vicinity of a sensor chip surface in which the ligands are immobilized, allowing the detection of biomolecules (analytes) interacting with the ligand. If the ligand is single-stranded biotinylated DNA, SPR technology can easily monitor DNA-DNA hybridization in real time (Feriotto et al., 2003).

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Feriotto et al. (2003) designed and tested a protocol for SPR based on biospecific interaction analysis (BIA) for quantitative determination of GMOs. The protocol is based on the immobilization of multiple PCR products in a single cell flow sensor chip covered with Streptavidin and the injection of specific probes. Zein products labeled with biotin and Bt176 were immobilized on sensor chip SA taking advantage of the Streptavidin-biotin interaction. The approach is based on the immobilization of the same cell targets flow of two PCR products obtained by multiplex PCR using primers tagged with biotin and subsequent analysis by consecutive injection of a compatible nucleotide probe. To produce doublestranded target gene sequences, multiplex PCR was done using genomic DNA pattern with an excess of Bt-R primers and ZM-r-biot with its own Bt-F and biot-ZM-F labeled with biotin. This was done to minimize the presence of biotin labeling of primers not incorporated into the PCR mixture. The final products of multiple PCR, zein and Bt-176, were purified with Microcon-30. The agarose gel analysis and direct sequencing of PCR products confirmed the specificity of the PCR reaction. The results were compared with Southern blot and quantitative PCR using ABI Prism 7700. 8.2 Techniques based on proteins identification 8.2.1 Enzyme-linked Immunosorbent Assay (or ELISA) In some cases the detection of GMOs in food is possible due to the presence of proteins encoded by transgenic sequences. This method only requires 4-6 hours to complete an analysis, it is easy to use and can effectively detect GMOs. Some of the restrictions on the use of ELISA for detecting transgenic proteins are denaturing of them during processing of food and the presence of false positive and negative reactions to other proteins. For these disadvantages, is estimated that the results of ELISA are reliable in 95% of cases (Hsu-Yang et al., 2001). 8.2.2 Lateral flow band This procedure represents an alternative to ELISA. The lateral flow band uses a combination of specific antibody to the protein of interest (capture antibody) with a conjugated antibody that is able to generate a colorimetric reaction (called detection antibody), which allows to visualize the presence of protein desired in the sample.

9. Conclusion and perspectives Detection of transgenic residues in foods become a very actual theme because the increase of products derived from genetically modified organisms in the market shelves and the consumer demands for more strict regulations about labeling of this kind of products. Literature revised reports that short DNA trangenic sequence remain without degradation during most of the food steps processing, at the present time have been developed different techniques which are able to identify small traces of transgenic DNA or proteins. When soybean-based foods were elaborated using transgenic soybean, it was observed that the transgenic DNA and proteins were not degraded during the processing of these kinds of foods. In addition, it was found that the soybean-based commercial food processing did not affect the persistence of transgenic residues in food. With the development of multiplex PCR, it was possible to detect more efficient transgenic residues in plants and soybeanbased foods.

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10. Acknowledgments The authors acknowledge the financial support provided by the Mexican Agency of Environmental and Natural Resources (SEMARNAT) through the projects: SEMARNATCONACYT2004-C01-00342, SEMARNAT-CONACYT2008-C01-108421and the project CONACYT-FOMIX COAH-2002-C01-4576. MLCG, CFG and VNGL thanks to CONACYT for the financial support during their MSc. Studies.

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